Persistent network model diagnostics - balanced statistics

This file shows diagnostics for persistent network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced race/ethnicity mixing matrices. In addition to adjusting race-specific degree, we adjusted the regional degree to be the weighted average of race/ethnicity-specific degrees. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.

Load packages and model fits

rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")
## Loading required package: lattice
## Loading required package: RColorBrewer
library("knitr")
library("kableExtra")
library("here")
## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMP
load(file = here("Model fits and simulations/Fit tests and debugging/est/fit.p.buildup.bal.rda"))

Model terms and control settings

Model terms and target statistics
Terms Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
edges 2017.5 2017.5 2017.5 2017.5 2017.5 2017.5 2017.5 2017.5
nodefactor.deg.main.1 NA NA NA 1699.0 1699.0 1699.0 1699.0 1699.0
nodefactor.race..wa.B NA 285.5 285.5 285.5 285.5 285.5 285.5 285.5
nodefactor.race..wa.H NA 605.3 605.3 605.3 605.3 605.3 605.3 605.3
nodefactor.region.EW NA NA NA NA 424.5 424.5 424.5 424.5
nodefactor.region.OW NA NA NA NA 1312.6 1312.6 1312.6 1312.6
concurrent NA NA NA NA NA NA 1384.0 1384.0
nodematch.race..wa.B NA NA 8.5 8.5 8.5 8.5 8.5 8.5
nodematch.race..wa.H NA NA 51.2 51.2 51.2 51.2 51.2 51.2
nodematch.race..wa.O NA NA 1247.1 1247.1 1247.1 1247.1 1247.1 1247.1
nodematch.region NA NA NA NA NA NA NA 1614.0
absdiff.sqrt.age NA NA NA NA NA 1664.8 1664.8 1664.8
degrange 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
nodematch.role.class.I -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf
nodematch.role.class.R -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf

The control settings for these models are:

set.control.ergm = ccontrol.ergm(MCMC.interval = 1e+5,
                                 MCMC.samplesize = 7500,
                                 MCMC.burnin = 1e+6,
                                 MPLE.max.dyad.types = 1e+7,
                                 init.method = "zeros",
                                 MCMLE.maxit = 400,
                                 parallel = np/2,
                                 parallel.type="PSOCK"))

MCMC diagnostics

Model 1

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##           Mean             SD       Naive SE Time-series SE 
##        -0.8468        40.4556         0.2336         0.2328 
## 
## 2. Quantiles for each variable:
## 
##  2.5%   25%   50%   75% 97.5% 
## -79.5 -28.5  -0.5  26.5  79.5 
## 
## 
## Sample statistics cross-correlations:
##       edges
## edges     1
## 
## Sample statistics auto-correlation:
## Chain 1 
##                 edges
## Lag 0      1.00000000
## Lag 1e+05 -0.02696659
## Lag 2e+05 -0.02004393
## Lag 3e+05  0.01311327
## Lag 4e+05  0.00778886
## Lag 5e+05 -0.01596866
## Chain 2 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.007778250
## Lag 2e+05  0.002992239
## Lag 3e+05 -0.030414501
## Lag 4e+05  0.022742004
## Lag 5e+05 -0.007225642
## Chain 3 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.008358496
## Lag 2e+05  0.005713489
## Lag 3e+05  0.001082667
## Lag 4e+05  0.003699070
## Lag 5e+05 -0.012638623
## Chain 4 
##                 edges
## Lag 0      1.00000000
## Lag 1e+05 -0.02196361
## Lag 2e+05  0.01836192
## Lag 3e+05 -0.01450819
## Lag 4e+05 -0.02158209
## Lag 5e+05  0.01242602
## Chain 5 
##                   edges
## Lag 0      1.000000e+00
## Lag 1e+05  9.814999e-03
## Lag 2e+05  3.249351e-03
## Lag 3e+05 -1.136881e-02
## Lag 4e+05  2.727052e-02
## Lag 5e+05 -6.478774e-05
## Chain 6 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.014358682
## Lag 2e+05 -0.001337833
## Lag 3e+05  0.003429987
## Lag 4e+05  0.013270079
## Lag 5e+05 -0.002446943
## Chain 7 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.002834492
## Lag 2e+05  0.017856358
## Lag 3e+05  0.025845842
## Lag 4e+05 -0.006661281
## Lag 5e+05  0.013340348
## Chain 8 
##                   edges
## Lag 0      1.0000000000
## Lag 1e+05  0.0070005526
## Lag 2e+05  0.0130928264
## Lag 3e+05  0.0005163194
## Lag 4e+05 -0.0034014384
## Lag 5e+05 -0.0190616365
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## 0.07428 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.9407842 
## Joint P-value (lower = worse):  0.9443706 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.1574 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.8749107 
## Joint P-value (lower = worse):  0.8743597 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.4119 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.6804084 
## Joint P-value (lower = worse):  0.6769639 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.5496 
## 
## Individual P-values (lower = worse):
##    edges 
## 0.582616 
## Joint P-value (lower = worse):  0.5714885 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.6912 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.4894154 
## Joint P-value (lower = worse):  0.4917554 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.2149 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.8298477 
## Joint P-value (lower = worse):  0.8316306 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## -1.084 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.2785084 
## Joint P-value (lower = worse):  0.304045 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.8128 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.4163452 
## Joint P-value (lower = worse):  0.431546 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 2

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                         Mean    SD Naive SE Time-series SE
## edges                 2.7829 40.49  0.23377        0.23443
## nodefactor.race..wa.B 0.4447 16.19  0.09348        0.09421
## nodefactor.race..wa.H 0.1001 23.63  0.13642        0.14044
## 
## 2. Quantiles for each variable:
## 
##                         2.5%    25%     50%   75% 97.5%
## edges                 -76.50 -24.50  2.5000 29.50 82.50
## nodefactor.race..wa.B -30.52 -10.52  0.4832 11.48 32.48
## nodefactor.race..wa.H -45.34 -16.34 -0.3400 15.66 46.66
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000            0.34641019
## nodefactor.race..wa.B 0.3464102            1.00000000
## nodefactor.race..wa.H 0.4751787            0.07918742
##                       nodefactor.race..wa.H
## edges                            0.47517872
## nodefactor.race..wa.B            0.07918742
## nodefactor.race..wa.H            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.023232748         -0.0034959916           0.017875514
## Lag 2e+05 -0.001983736         -0.0168220279           0.008358084
## Lag 3e+05 -0.015795757          0.0003010341           0.014590133
## Lag 4e+05  0.001802848          0.0020808864           0.004937218
## Lag 5e+05  0.019669158         -0.0173034648          -0.001465206
## Chain 2 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0045421940          -0.018095630           0.025873746
## Lag 2e+05 -0.0145265213          -0.017655310           0.024357123
## Lag 3e+05 -0.0009146192          -0.007574214          -0.005346450
## Lag 4e+05  0.0116077823          -0.012094749           0.011126297
## Lag 5e+05  0.0163075780           0.006546645           0.008242495
## Chain 3 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05  0.003414915            0.02665128           0.005827806
## Lag 2e+05  0.025989936            0.02232682          -0.003180250
## Lag 3e+05  0.012735473            0.01097319           0.006196804
## Lag 4e+05  0.016153042           -0.01542873           0.004917451
## Lag 5e+05 -0.006425709           -0.01618662           0.024526624
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.001585530          -0.011137271           0.028450240
## Lag 2e+05  0.012156279           0.033192932           0.037780174
## Lag 3e+05 -0.000473927          -0.005939590          -0.001885223
## Lag 4e+05 -0.010783129          -0.001187619          -0.028444440
## Lag 5e+05 -0.018093074          -0.009314924          -0.035647671
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.005190244          -0.001857585           0.038500361
## Lag 2e+05  0.015650195          -0.002226692          -0.003686892
## Lag 3e+05 -0.006203796          -0.001689881           0.010046951
## Lag 4e+05  0.008492773          -0.012492375           0.008159489
## Lag 5e+05  0.022130337           0.002603665           0.002554883
## Chain 6 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.002224462           0.002457265          -0.008035761
## Lag 2e+05  0.019293265           0.010460472           0.007574993
## Lag 3e+05  0.026542859          -0.004913981          -0.001679060
## Lag 4e+05 -0.021616873           0.004677633          -0.028355282
## Lag 5e+05 -0.018191063          -0.002328437           0.042905056
## Chain 7 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000          1.0000000000           1.000000000
## Lag 1e+05  0.0080093855          0.0255715296           0.024469357
## Lag 2e+05  0.0063013776          0.0003394649          -0.007383481
## Lag 3e+05 -0.0204270038         -0.0095750156           0.009527346
## Lag 4e+05  0.0005862355          0.0231250718          -0.040485758
## Lag 5e+05  0.0190976392         -0.0137702329           0.009395955
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.018646315           0.017610496          -0.011327738
## Lag 2e+05  0.005205172          -0.008957189           0.044683441
## Lag 3e+05  0.017896133          -0.021864442           0.009138015
## Lag 4e+05 -0.006663206          -0.009502223           0.032640846
## Lag 5e+05 -0.015510566          -0.008426728          -0.003306575
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.1531                0.7950               -0.3341 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.8782844             0.4266154             0.7383364 
## Joint P-value (lower = worse):  0.8157513 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.6462               -1.6511               -0.2238 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.51814860            0.09870959            0.82289505 
## Joint P-value (lower = worse):  0.5485255 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.5895                1.2290                0.4294 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.5554960             0.2190740             0.6676638 
## Joint P-value (lower = worse):  0.3035522 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.1531               -0.4291                1.2294 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.8783140             0.6678485             0.2189118 
## Joint P-value (lower = worse):  0.5504082 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.1770               -0.2772                0.6326 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.8594813             0.7816439             0.5269760 
## Joint P-value (lower = worse):  0.9310504 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.3689                0.1673                1.1248 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.1710220             0.8671699             0.2606925 
## Joint P-value (lower = worse):  0.4331449 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.1796                0.5687                1.2053 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.8574286             0.5695450             0.2280723 
## Joint P-value (lower = worse):  0.3901039 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.8903                1.0652                0.8932 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.3732863             0.2867882             0.3717392 
## Joint P-value (lower = worse):  0.5993255 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 3

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                            Mean     SD Naive SE Time-series SE
## edges                 1.5222667 40.106  0.23155        0.23381
## nodefactor.race..wa.B 0.0002333 16.100  0.09295        0.09293
## nodefactor.race..wa.H 0.2496333 23.714  0.13691        0.13681
## nodematch.race..wa.B  0.0009844  2.877  0.01661        0.01669
## nodematch.race..wa.H  0.0717697  6.957  0.04016        0.04017
## nodematch.race..wa.O  1.2760540 32.645  0.18848        0.18977
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%    75% 97.5%
## edges                 -77.50 -25.500  1.5000 28.500 80.50
## nodefactor.race..wa.B -31.52 -10.517 -0.5168 10.483 31.48
## nodefactor.race..wa.H -46.34 -15.340 -0.3400 16.660 46.66
## nodematch.race..wa.B   -5.48  -2.480 -0.4798  1.520  5.52
## nodematch.race..wa.H  -13.18  -4.181 -0.1815  4.819 13.82
## nodematch.race..wa.O  -62.08 -21.081  0.9192 23.919 65.92
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.race..wa.B
## edges                 1.00000000           0.344861890
## nodefactor.race..wa.B 0.34486189           1.000000000
## nodefactor.race..wa.H 0.47506781           0.112449321
## nodematch.race..wa.B  0.05763626           0.318548319
## nodematch.race..wa.H  0.12962301          -0.002223137
## nodematch.race..wa.O  0.78300875          -0.023830062
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                           0.475067813          0.057636263
## nodefactor.race..wa.B           0.112449321          0.318548319
## nodefactor.race..wa.H           1.000000000         -0.001642114
## nodematch.race..wa.B           -0.001642114          1.000000000
## nodematch.race..wa.H            0.496849030         -0.003865249
## nodematch.race..wa.O           -0.029537128         -0.002310991
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.129623012          0.783008750
## nodefactor.race..wa.B         -0.002223137         -0.023830062
## nodefactor.race..wa.H          0.496849030         -0.029537128
## nodematch.race..wa.B          -0.003865249         -0.002310991
## nodematch.race..wa.H           1.000000000          0.006342064
## nodematch.race..wa.O           0.006342064          1.000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.015843825           0.004476658         -0.0123840990
## Lag 2e+05  0.022830608           0.026081597         -0.0004758322
## Lag 3e+05 -0.010266978           0.022497876         -0.0074809756
## Lag 4e+05 -0.010176100           0.007010111          0.0027888468
## Lag 5e+05  0.001646553          -0.003565484         -0.0059172417
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05          0.029507659         0.0044240860          0.011697669
## Lag 2e+05          0.029280846        -0.0002156142          0.030530143
## Lag 3e+05         -0.017892476         0.0078809289         -0.029823488
## Lag 4e+05         -0.019757221        -0.0013986078          0.002205789
## Lag 5e+05         -0.002560023         0.0041369884          0.008078494
## Chain 2 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05  0.016941891           0.019706497            0.03637509
## Lag 2e+05 -0.005247894           0.038019267            0.01483554
## Lag 3e+05  0.004445855           0.008358530           -0.02340338
## Lag 4e+05  0.002285735          -0.007735001           -0.03433566
## Lag 5e+05 -0.026065446          -0.012895606           -0.01203539
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.005197915          0.021035681          0.014982553
## Lag 2e+05         -0.032404844          0.002873927         -0.020067088
## Lag 3e+05          0.008607397         -0.019044091          0.005422555
## Lag 4e+05         -0.001680810          0.010554109          0.014134414
## Lag 5e+05         -0.007416965          0.003038953         -0.037716053
## Chain 3 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05  0.013824569            0.02447254          -0.006410762
## Lag 2e+05  0.012501892            0.01085864          -0.022218053
## Lag 3e+05 -0.005301712           -0.01740529           0.008131581
## Lag 4e+05 -0.003091473           -0.01582085          -0.011415356
## Lag 5e+05 -0.008102035           -0.03086505          -0.016850482
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.004759781          0.014603155         -0.004154625
## Lag 2e+05         -0.014105057         -0.010864395         -0.012818269
## Lag 3e+05         -0.012762842         -0.002703005          0.008316462
## Lag 4e+05         -0.011327110          0.006003129         -0.017629108
## Lag 5e+05         -0.020346747         -0.011706623         -0.006805634
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.045313579          -0.025116085           0.005863421
## Lag 2e+05 -0.010907397          -0.007486236          -0.001959731
## Lag 3e+05  0.018231035           0.013221813          -0.006372782
## Lag 4e+05 -0.001195779          -0.028200413           0.004864977
## Lag 5e+05 -0.016160695           0.009750146          -0.001770768
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.018438565         -0.013820120          0.054690503
## Lag 2e+05         -0.006101967         -0.011768178          0.004726460
## Lag 3e+05          0.002448923         -0.009652485          0.001804332
## Lag 4e+05         -0.012426713          0.011274481          0.008135527
## Lag 5e+05          0.026216660         -0.008733913         -0.021894075
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.012817636          -0.005102629          -0.002837681
## Lag 2e+05  0.045322769           0.010927296           0.016531982
## Lag 3e+05 -0.028163274          -0.038111845           0.011669294
## Lag 4e+05  0.007378126          -0.007475069          -0.015207659
## Lag 5e+05 -0.005340183           0.005197425          -0.021014110
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000           1.00000000
## Lag 1e+05          0.012165410         -0.004399127           0.03075894
## Lag 2e+05          0.017837163         -0.003784671           0.01654131
## Lag 3e+05          0.020784562          0.018849655          -0.01602348
## Lag 4e+05          0.009287822         -0.018791366          -0.02407606
## Lag 5e+05          0.001040370          0.012324844          -0.02454469
## Chain 6 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0067476866          -0.009602250          -0.008286705
## Lag 2e+05  0.0006613100          -0.019757738          -0.005420505
## Lag 3e+05 -0.0005028809           0.024108199           0.011812553
## Lag 4e+05 -0.0009809733           0.006131602           0.014168785
## Lag 5e+05  0.0029458956           0.003573553           0.007827066
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.011395498          0.001106497         0.0198841455
## Lag 2e+05          0.006682362          0.008362255         0.0077796532
## Lag 3e+05         -0.017115036          0.001273001        -0.0002402171
## Lag 4e+05          0.016912910          0.010364218        -0.0021866985
## Lag 5e+05         -0.014951882         -0.030688079        -0.0275065489
## Chain 7 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.001787205           0.002876734           0.005453866
## Lag 2e+05  0.004834070           0.025302369           0.006328637
## Lag 3e+05 -0.010475370          -0.014976960           0.020387675
## Lag 4e+05  0.011461063           0.029679329           0.014062944
## Lag 5e+05  0.016661217           0.005946060           0.009176090
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000         1.0000000000
## Lag 1e+05          -0.02562748          0.001547637        -0.0092781256
## Lag 2e+05           0.03018740         -0.005831895        -0.0026172231
## Lag 3e+05          -0.01769355          0.021112238        -0.0391936562
## Lag 4e+05          -0.01323315          0.006628095         0.0003841655
## Lag 5e+05          -0.00647332          0.030100880         0.0269388531
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.004602997          -0.006864796           0.002524391
## Lag 2e+05  0.014423581           0.005204988           0.020539203
## Lag 3e+05 -0.007827058           0.014588442           0.004986211
## Lag 4e+05 -0.015182835           0.010855256          -0.020622392
## Lag 5e+05 -0.023647507          -0.021803824           0.011741422
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05         -0.009783396        -0.0078549289          0.014854467
## Lag 2e+05          0.012242667         0.0191136821          0.003589613
## Lag 3e+05          0.004797528        -0.0003533467         -0.005882155
## Lag 4e+05          0.017046128         0.0163171595          0.002185058
## Lag 5e+05         -0.024226989         0.0073939906         -0.018867042
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.0586                0.8460                0.2044 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.5267               -2.0277                0.9819 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.28979366            0.39756485            0.83802019 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.59839016            0.04258782            0.32616319 
## Joint P-value (lower = worse):  0.1025072 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.8268                0.6703                0.2626 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.2348               -0.3483               -1.2736 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4083639             0.5026756             0.7928784 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8144008             0.7276253             0.2028033 
## Joint P-value (lower = worse):  0.7204317 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                -1.436                 1.212                -1.786 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                 1.916                -1.288                -1.349 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.15110888            0.22543588            0.07416461 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.05534429            0.19778952            0.17744405 
## Joint P-value (lower = worse):  0.2219073 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               1.24162              -0.84516              -0.01459 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -1.70256              -1.02005               1.61859 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.21437684            0.39802206            0.98835719 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.08865069            0.30770394            0.10553590 
## Joint P-value (lower = worse):  0.3567958 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.7007               -0.7338               -0.5113 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.2605               -0.3475                1.6395 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4834803             0.4630604             0.6091362 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.7944457             0.7281874             0.1011013 
## Joint P-value (lower = worse):  0.8505518 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.01658              -0.06448              -0.02851 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.80942               1.03286               0.18146 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.9867695             0.9485907             0.9772553 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.4182714             0.3016712             0.8560040 
## Joint P-value (lower = worse):  0.881198 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -1.73041              -1.98808               0.09989 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.48089               0.44096              -1.43276 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.08355794            0.04680232            0.92043056 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.63059246            0.65923957            0.15192573 
## Joint P-value (lower = worse):  0.3536858 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.8216                2.2475                0.2049 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.4535                0.1956                1.1446 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.06851677            0.02460811            0.83766066 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.65016843            0.84494180            0.25239195 
## Joint P-value (lower = worse):  0.2052411 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 4

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                  0.15977 39.966  0.23074        0.23260
## nodefactor.deg.main.1 -0.20630 45.345  0.26180        0.26047
## nodefactor.race..wa.B  0.03713 16.053  0.09268        0.09411
## nodefactor.race..wa.H  0.14393 23.553  0.13598        0.13686
## nodematch.race..wa.B  -0.05322  2.884  0.01665        0.01623
## nodematch.race..wa.H   0.06987  6.975  0.04027        0.04013
## nodematch.race..wa.O   0.11812 32.665  0.18859        0.18896
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%      50%    75% 97.5%
## edges                 -78.50 -26.500  0.50000 27.500 78.50
## nodefactor.deg.main.1 -89.00 -31.000  0.00000 30.000 89.00
## nodefactor.race..wa.B -30.52 -10.517 -0.51680 10.483 32.48
## nodefactor.race..wa.H -45.34 -15.340 -0.34000 15.660 46.66
## nodematch.race..wa.B   -5.48  -2.480 -0.47985  1.520  6.52
## nodematch.race..wa.H  -13.18  -5.181 -0.18150  4.819 13.82
## nodematch.race..wa.O  -64.08 -22.081 -0.08078 21.919 63.92
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.75435602
## nodefactor.deg.main.1 0.75435602            1.00000000
## nodefactor.race..wa.B 0.33851232            0.22364886
## nodefactor.race..wa.H 0.46542335            0.39531235
## nodematch.race..wa.B  0.05682146            0.03412326
## nodematch.race..wa.H  0.12960788            0.11737225
## nodematch.race..wa.O  0.78589064            0.58049480
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                           0.338512322           0.465423354
## nodefactor.deg.main.1           0.223648857           0.395312348
## nodefactor.race..wa.B           1.000000000           0.102019603
## nodefactor.race..wa.H           0.102019603           1.000000000
## nodematch.race..wa.B            0.318708266           0.001460072
## nodematch.race..wa.H           -0.009040549           0.501713153
## nodematch.race..wa.O           -0.026690472          -0.034467039
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                          0.056821459          0.129607878
## nodefactor.deg.main.1          0.034123262          0.117372255
## nodefactor.race..wa.B          0.318708266         -0.009040549
## nodefactor.race..wa.H          0.001460072          0.501713153
## nodematch.race..wa.B           1.000000000          0.011906581
## nodematch.race..wa.H           0.011906581          1.000000000
## nodematch.race..wa.O          -0.001822617          0.006844409
##                       nodematch.race..wa.O
## edges                          0.785890636
## nodefactor.deg.main.1          0.580494801
## nodefactor.race..wa.B         -0.026690472
## nodefactor.race..wa.H         -0.034467039
## nodematch.race..wa.B          -0.001822617
## nodematch.race..wa.H           0.006844409
## nodematch.race..wa.O           1.000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.009293063           0.008011998          -0.020722780
## Lag 2e+05 -0.018081756          -0.006216810           0.005627376
## Lag 3e+05  0.011383663           0.026950876          -0.010029064
## Lag 4e+05 -0.013430660           0.003339623          -0.003574175
## Lag 5e+05  0.003813957           0.010677571           0.013976224
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05         -0.0080016775         -0.001476886         -0.019302207
## Lag 2e+05         -0.0065974590          0.014037323         -0.008173921
## Lag 3e+05         -0.0023484951         -0.044061131         -0.011715521
## Lag 4e+05          0.0001880001          0.018967260          0.006887641
## Lag 5e+05         -0.0234953632          0.014294712         -0.016379106
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.002098923
## Lag 2e+05         -0.017595895
## Lag 3e+05          0.027289908
## Lag 4e+05         -0.014830819
## Lag 5e+05          0.001936321
## Chain 2 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.018237694          -0.040697309           0.005015403
## Lag 2e+05  0.020465137          -0.006178059           0.018563952
## Lag 3e+05  0.023261821           0.002307541          -0.009131595
## Lag 4e+05 -0.001057455          -0.007313872          -0.003350575
## Lag 5e+05  0.013408178           0.012545281           0.006513220
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05          -0.012684860         0.0010828510         -0.028259959
## Lag 2e+05          -0.003139790        -0.0133935329          0.016644511
## Lag 3e+05           0.022906648        -0.0001421568          0.013071676
## Lag 4e+05           0.008604890        -0.0317307004         -0.009207429
## Lag 5e+05           0.006506597        -0.0171964991         -0.005462904
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.025331549
## Lag 2e+05         -0.004345648
## Lag 3e+05          0.033991344
## Lag 4e+05         -0.002791178
## Lag 5e+05          0.012098907
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.001186623           0.005745008           0.001751251
## Lag 2e+05  0.018183195           0.014756930           0.039419008
## Lag 3e+05 -0.027444894          -0.012936803          -0.016645592
## Lag 4e+05 -0.014858704          -0.008436175           0.006630207
## Lag 5e+05 -0.006151160           0.033499247          -0.013271629
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.016620645         -0.010114631         -0.017944590
## Lag 2e+05           0.019775456          0.010546563         -0.008907330
## Lag 3e+05          -0.007675039         -0.041593110          0.010477988
## Lag 4e+05           0.013270999         -0.001443653          0.007536412
## Lag 5e+05          -0.004971104          0.025253299          0.002122503
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.008540368
## Lag 2e+05          0.006610328
## Lag 3e+05          0.004960897
## Lag 4e+05         -0.003118366
## Lag 5e+05         -0.001724229
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.000000e+00
## Lag 1e+05  0.022948940           0.003274508         -1.424741e-02
## Lag 2e+05 -0.006990688          -0.010982905          2.207110e-02
## Lag 3e+05  0.007820858          -0.008017556          6.099203e-03
## Lag 4e+05  0.027123351          -0.003068464          1.658893e-03
## Lag 5e+05 -0.016889843          -0.005636265          4.725629e-05
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.046468614         0.0186958556         -0.002738294
## Lag 2e+05          -0.004488874        -0.0002844727          0.001308469
## Lag 3e+05          -0.014537653        -0.0066132463         -0.016067678
## Lag 4e+05          -0.018374810         0.0166350271         -0.010933197
## Lag 5e+05          -0.012693726         0.0175186586         -0.003243118
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.001655538
## Lag 2e+05         -0.003940169
## Lag 3e+05         -0.007186782
## Lag 4e+05          0.032758649
## Lag 5e+05          0.002577675
## Chain 5 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05 -0.01993311           0.016288313          -0.008220466
## Lag 2e+05 -0.02196141          -0.001563550           0.003173376
## Lag 3e+05  0.02122909           0.002312400           0.006011395
## Lag 4e+05 -0.01337656          -0.016383689          -0.026885529
## Lag 5e+05  0.01363626          -0.001660243          -0.020797532
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.001401362         -0.041404272          0.012969389
## Lag 2e+05           0.009289617         -0.016352658          0.002479853
## Lag 3e+05           0.025378040         -0.008827061          0.006612844
## Lag 4e+05          -0.005259818          0.004097936         -0.013765971
## Lag 5e+05          -0.028221806         -0.021059962         -0.018229630
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.010578101
## Lag 2e+05         -0.011190862
## Lag 3e+05         -0.020801603
## Lag 4e+05         -0.007749814
## Lag 5e+05          0.009735176
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.003653517         -0.0015679033          -0.017660505
## Lag 2e+05  0.014558174          0.0005850216           0.009973016
## Lag 3e+05  0.017305341          0.0142406622           0.014533012
## Lag 4e+05  0.012583340         -0.0064996687           0.051125473
## Lag 5e+05 -0.003094327         -0.0121423641          -0.002365358
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05          -0.034175979         -0.005700256        -0.0047655775
## Lag 2e+05           0.023990043          0.021851469         0.0175416039
## Lag 3e+05           0.004251331          0.006276522        -0.0173563281
## Lag 4e+05           0.010362153         -0.013613391         0.0034778828
## Lag 5e+05          -0.009587600         -0.015199305        -0.0009915874
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.012566762
## Lag 2e+05          0.023156755
## Lag 3e+05          0.013737604
## Lag 4e+05          0.004362603
## Lag 5e+05         -0.031385819
## Chain 7 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0     1.000000000           1.000000000           1.000000000
## Lag 1e+05 0.019435737           0.011850949           0.003078150
## Lag 2e+05 0.005161988           0.007829524           0.002324990
## Lag 3e+05 0.037654383           0.027480585          -0.003468542
## Lag 4e+05 0.005361665          -0.004993771          -0.016217411
## Lag 5e+05 0.003055633          -0.001018698          -0.009813130
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.004052237          0.032301484          0.001126623
## Lag 2e+05          -0.015614977         -0.009034676         -0.013694880
## Lag 3e+05           0.005133637         -0.027066454          0.002654601
## Lag 4e+05          -0.016010975         -0.038736783         -0.006065059
## Lag 5e+05          -0.006401800         -0.001323357          0.015247604
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0345187188
## Lag 2e+05         0.0146041642
## Lag 3e+05         0.0295160548
## Lag 4e+05        -0.0001618107
## Lag 5e+05        -0.0024724736
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.013131415           0.017879715          -0.015151666
## Lag 2e+05  0.012990075          -0.015551721           0.037727969
## Lag 3e+05  0.007594693          -0.005010625           0.014582723
## Lag 4e+05 -0.023607472          -0.009327895           0.017416738
## Lag 5e+05  0.022588059          -0.005881848          -0.001589607
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.036062130          0.013677284          0.018135151
## Lag 2e+05           0.015572939          0.005823085          0.018700011
## Lag 3e+05           0.013693404         -0.008601553          0.003613198
## Lag 4e+05           0.001012469         -0.007943640          0.002009244
## Lag 5e+05          -0.009931664          0.002792836         -0.015222331
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.023766282
## Lag 2e+05          0.012977398
## Lag 3e+05         -0.011083680
## Lag 4e+05          0.006002424
## Lag 5e+05          0.035954519
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.7384                0.4747               -0.6884 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.4355               -0.6245                0.8329 
##  nodematch.race..wa.O 
##                0.1942 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.4602634             0.6350053             0.4912313 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.1511316             0.5323010             0.4048863 
##  nodematch.race..wa.O 
##             0.8460135 
## Joint P-value (lower = worse):  0.8461884 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               1.81764               1.00605               1.14825 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.40055               0.03797              -0.20965 
##  nodematch.race..wa.O 
##               1.77587 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.06911935            0.31439271            0.25086723 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.68874942            0.96971459            0.83394117 
##  nodematch.race..wa.O 
##            0.07575434 
## Joint P-value (lower = worse):  0.3980385 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -2.3303               -1.6643               -0.5194 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##               -1.5694                0.2624               -0.3911 
##  nodematch.race..wa.O 
##               -1.4138 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.01979061            0.09605812            0.60346459 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.11656477            0.79300506            0.69575892 
##  nodematch.race..wa.O 
##            0.15741393 
## Joint P-value (lower = worse):  0.4895735 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.5028                0.6917               -0.3741 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.1209               -1.5897               -0.0983 
##  nodematch.race..wa.O 
##               -0.1746 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6151032             0.4890947             0.7083326 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.2623309             0.1119015             0.9216978 
##  nodematch.race..wa.O 
##             0.8613914 
## Joint P-value (lower = worse):  0.7396147 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.15661              -0.09658               0.43925 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.18341               1.59057              -0.18995 
##  nodematch.race..wa.O 
##               0.14598 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.8755495             0.9230637             0.6604794 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.8544742             0.1117072             0.8493477 
##  nodematch.race..wa.O 
##             0.8839356 
## Joint P-value (lower = worse):  0.6088548 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.69307               0.72823               0.94701 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##              -1.23936               0.04255              -0.44423 
##  nodematch.race..wa.O 
##               1.50225 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.4882660             0.4664703             0.3436337 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.2152140             0.9660640             0.6568737 
##  nodematch.race..wa.O 
##             0.1330321 
## Joint P-value (lower = worse):  0.4732055 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.3352                1.3624                1.6588 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.8390                0.7001                2.1448 
##  nodematch.race..wa.O 
##               -0.8624 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.73745236            0.17306928            0.09715780 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.06591712            0.48384679            0.03197160 
##  nodematch.race..wa.O 
##            0.38848316 
## Joint P-value (lower = worse):  0.1540432 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.11368               0.21635               0.03121 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##              -1.28667              -1.76471              -0.23792 
##  nodematch.race..wa.O 
##               0.16016 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.90949261            0.82871606            0.97510166 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.19821006            0.07761215            0.81194331 
##  nodematch.race..wa.O 
##            0.87275159 
## Joint P-value (lower = worse):  0.2649218 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 5

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                  0.15557 40.043  0.23119        0.23120
## nodefactor.deg.main.1  0.01103 45.033  0.26000        0.25999
## nodefactor.race..wa.B -0.07180 16.122  0.09308        0.09379
## nodefactor.race..wa.H  0.33197 23.643  0.13650        0.13716
## nodefactor.region.EW   0.66843 20.094  0.11601        0.11516
## nodefactor.region.OW  -0.22487 38.261  0.22090        0.22090
## nodematch.race..wa.B  -0.11268  2.853  0.01647        0.01638
## nodematch.race..wa.H  -0.03306  6.960  0.04018        0.03948
## nodematch.race..wa.O  -0.29365 32.780  0.18925        0.18787
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%      50%    75% 97.5%
## edges                 -77.50 -27.500  0.50000 27.500 78.50
## nodefactor.deg.main.1 -88.00 -30.000  0.00000 30.000 89.00
## nodefactor.race..wa.B -31.52 -10.517 -0.51680 10.483 31.48
## nodefactor.race..wa.H -46.34 -15.340  0.66000 15.660 47.66
## nodefactor.region.EW  -38.48 -13.482  0.51800 14.518 40.52
## nodefactor.region.OW  -74.59 -25.585 -0.58550 25.415 75.41
## nodematch.race..wa.B   -5.48  -2.480 -0.47985  1.520  5.52
## nodematch.race..wa.H  -13.18  -5.181 -0.18150  4.819 13.82
## nodematch.race..wa.O  -64.08 -23.081 -0.08078 21.919 64.92
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.75345727
## nodefactor.deg.main.1 0.75345727            1.00000000
## nodefactor.race..wa.B 0.33845329            0.21689108
## nodefactor.race..wa.H 0.46819134            0.40310865
## nodefactor.region.EW  0.41379077            0.32375711
## nodefactor.region.OW  0.68390840            0.52010474
## nodematch.race..wa.B  0.05520395            0.03302513
## nodematch.race..wa.H  0.12805733            0.13228423
## nodematch.race..wa.O  0.78250545            0.57760841
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.33845329           0.468191338
## nodefactor.deg.main.1            0.21689108           0.403108649
## nodefactor.race..wa.B            1.00000000           0.111030569
## nodefactor.race..wa.H            0.11103057           1.000000000
## nodefactor.region.EW             0.08695070           0.298396112
## nodefactor.region.OW             0.21378314           0.302690399
## nodematch.race..wa.B             0.31689084          -0.007312129
## nodematch.race..wa.H            -0.01121136           0.501694774
## nodematch.race..wa.O            -0.03362265          -0.036938569
##                       nodefactor.region.EW nodefactor.region.OW
## edges                          0.413790766           0.68390840
## nodefactor.deg.main.1          0.323757113           0.52010474
## nodefactor.race..wa.B          0.086950699           0.21378314
## nodefactor.race..wa.H          0.298396112           0.30269040
## nodefactor.region.EW           1.000000000           0.13004633
## nodefactor.region.OW           0.130046326           1.00000000
## nodematch.race..wa.B          -0.004221274           0.03056970
## nodematch.race..wa.H           0.110545603           0.07830264
## nodematch.race..wa.O           0.287710994           0.55195747
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                         0.0552039514          0.128057328
## nodefactor.deg.main.1         0.0330251299          0.132284233
## nodefactor.race..wa.B         0.3168908392         -0.011211360
## nodefactor.race..wa.H        -0.0073121289          0.501694774
## nodefactor.region.EW         -0.0042212743          0.110545603
## nodefactor.region.OW          0.0305697022          0.078302636
## nodematch.race..wa.B          1.0000000000         -0.002071752
## nodematch.race..wa.H         -0.0020717517          1.000000000
## nodematch.race..wa.O         -0.0009486707          0.004490205
##                       nodematch.race..wa.O
## edges                         0.7825054455
## nodefactor.deg.main.1         0.5776084054
## nodefactor.race..wa.B        -0.0336226496
## nodefactor.race..wa.H        -0.0369385690
## nodefactor.region.EW          0.2877109936
## nodefactor.region.OW          0.5519574695
## nodematch.race..wa.B         -0.0009486707
## nodematch.race..wa.H          0.0044902047
## nodematch.race..wa.O          1.0000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.0155776719         -0.0168773039           0.005430397
## Lag 2e+05  0.0027718609         -0.0001376195          -0.029364591
## Lag 3e+05  0.0004772579          0.0089367378           0.015467548
## Lag 4e+05 -0.0008029413         -0.0058546594          -0.014468658
## Lag 5e+05 -0.0079982515         -0.0172002366           0.021607746
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.006537826         -0.008071934          0.001327376
## Lag 2e+05           0.026296447          0.014683188          0.005850599
## Lag 3e+05           0.004118184          0.019160936          0.006175374
## Lag 4e+05           0.009716777          0.018350626         -0.018257224
## Lag 5e+05           0.017242548          0.003867631         -0.024216917
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05          0.017887494         0.0195345824         -0.010411918
## Lag 2e+05          0.008976638         0.0059502466          0.005590610
## Lag 3e+05          0.006751088        -0.0004818955          0.004119323
## Lag 4e+05          0.022051434         0.0053382565         -0.005117515
## Lag 5e+05         -0.032673970         0.0046287553          0.003539979
## Chain 2 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0009359386           0.010891170          -0.010269036
## Lag 2e+05  0.0079056044           0.007410431          -0.015774792
## Lag 3e+05 -0.0326617413           0.006680648           0.019503027
## Lag 4e+05 -0.0018055029           0.005509050          -0.007932271
## Lag 5e+05  0.0012825739          -0.013797275          -0.012165708
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000         1.0000000000          1.000000000
## Lag 1e+05            0.01021976         0.0180414165          0.007544616
## Lag 2e+05           -0.01050902         0.0201989046         -0.020130129
## Lag 3e+05           -0.01810262        -0.0051635342         -0.023956593
## Lag 4e+05            0.01482104        -0.0080653834         -0.018622113
## Lag 5e+05           -0.04386961         0.0005156574          0.001199063
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000           1.00000000
## Lag 1e+05         -0.023006553          0.015284417           0.01541778
## Lag 2e+05          0.005514688         -0.021099801           0.01590598
## Lag 3e+05          0.015080449          0.008437308          -0.03454597
## Lag 4e+05         -0.014024572         -0.004507218          -0.01783006
## Lag 5e+05          0.004573674         -0.020195620           0.01366259
## Chain 3 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000          1.0000000000          1.0000000000
## Lag 1e+05 -0.0072106592         -0.0002629941         -0.0021847694
## Lag 2e+05 -0.0007276835          0.0210744912          0.0326632708
## Lag 3e+05 -0.0303260322         -0.0107311152          0.0007500169
## Lag 4e+05 -0.0070347787         -0.0061653078         -0.0059201589
## Lag 5e+05 -0.0120867226         -0.0118707273          0.0141423435
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.003248183        -0.0172346199         -0.011413453
## Lag 2e+05           0.018272537        -0.0509328592         -0.002297919
## Lag 3e+05          -0.013197218         0.0001011912         -0.016819210
## Lag 4e+05           0.006739119        -0.0044089465          0.002312680
## Lag 5e+05          -0.013426141         0.0020308061         -0.006129954
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000          1.000000000
## Lag 1e+05         -0.003279911          -0.01698751         -0.007029950
## Lag 2e+05         -0.030959165           0.01279362         -0.001144673
## Lag 3e+05         -0.001861938          -0.02260940         -0.031265420
## Lag 4e+05          0.016462522          -0.01311573          0.002561516
## Lag 5e+05          0.011212173          -0.01500510         -0.006671556
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.013419079          -0.003968737           0.007325640
## Lag 2e+05  0.005777806           0.008600556           0.012425807
## Lag 3e+05 -0.002403804          -0.011952942           0.001401465
## Lag 4e+05 -0.011825942           0.007599393           0.005656423
## Lag 5e+05  0.025765076           0.014499201           0.010274951
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05         -0.0045992927         -0.007215112         -0.013662434
## Lag 2e+05         -0.0170181626          0.001602423         -0.009471421
## Lag 3e+05         -0.0121272991         -0.018578935          0.026582384
## Lag 4e+05          0.0007868562         -0.022364469         -0.005325528
## Lag 5e+05          0.0194036923         -0.003293690          0.014394572
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.001753176          0.014883522         -0.005222238
## Lag 2e+05          0.026773877         -0.027377844          0.016009146
## Lag 3e+05         -0.003216809         -0.003241682          0.016157091
## Lag 4e+05         -0.006112816         -0.008519223          0.004229298
## Lag 5e+05         -0.009555178         -0.006692635          0.013894126
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.010542713           0.010177741          0.0001473984
## Lag 2e+05 -0.024039347          -0.013291550         -0.0115452943
## Lag 3e+05  0.008855302           0.002487855          0.0206501588
## Lag 4e+05 -0.004425617          -0.007962527         -0.0250465686
## Lag 5e+05 -0.004690670           0.019010122         -0.0075950975
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.002929805          0.001093819         -0.022160694
## Lag 2e+05          -0.018377018         -0.002393030         -0.003016368
## Lag 3e+05           0.007923728          0.001774896          0.006480680
## Lag 4e+05          -0.039232096         -0.006586827         -0.009124284
## Lag 5e+05          -0.004028321         -0.022057672          0.011136258
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.010688127          0.014437763         -0.005518893
## Lag 2e+05         -0.009000773          0.014114123         -0.027949934
## Lag 3e+05         -0.016616711         -0.006886396         -0.018890577
## Lag 4e+05          0.001552123         -0.014073027         -0.013864917
## Lag 5e+05          0.017188564         -0.031278642          0.017284321
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05  0.010654116           0.009152485           -0.01166566
## Lag 2e+05  0.006836376           0.017371837            0.01701649
## Lag 3e+05 -0.001310423           0.003289212            0.00730490
## Lag 4e+05 -0.012774622          -0.017055873           -0.01806714
## Lag 5e+05  0.008143080          -0.004749170           -0.01525660
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.034988414         -0.009637860          0.003001375
## Lag 2e+05           0.007064118          0.022320561         -0.024108999
## Lag 3e+05          -0.015083396         -0.030332067         -0.016385491
## Lag 4e+05           0.006745265          0.003943670         -0.004398145
## Lag 5e+05           0.010464293         -0.001394617         -0.013764165
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05          0.001756447         0.0002853853          0.003555721
## Lag 2e+05         -0.002491891         0.0154954242         -0.009143844
## Lag 3e+05          0.026049657        -0.0133481192          0.001831496
## Lag 4e+05         -0.013704124         0.0495370550         -0.015568125
## Lag 5e+05          0.006570685        -0.0016052403          0.028217604
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.022115509          -0.001161409          -0.010607769
## Lag 2e+05  0.006562722           0.002153508          -0.005158655
## Lag 3e+05 -0.007478078          -0.037022942           0.002618603
## Lag 4e+05  0.025670402           0.020364905           0.015157453
## Lag 5e+05 -0.013981280          -0.007319128           0.005140514
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000         1.0000000000          1.000000000
## Lag 1e+05         -0.0070675106        -0.0032213699          0.014046307
## Lag 2e+05          0.0497291058        -0.0106181781          0.007957787
## Lag 3e+05         -0.0055366954         0.0007378411         -0.017621268
## Lag 4e+05         -0.0004487348         0.0074009924          0.020561450
## Lag 5e+05          0.0161310212         0.0303864883         -0.034492764
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.008889246         -0.005219654          0.007864813
## Lag 2e+05         -0.015543786          0.020385866          0.008364800
## Lag 3e+05          0.015795089          0.011548535          0.020701867
## Lag 4e+05          0.014920328         -0.023197911          0.031904457
## Lag 5e+05         -0.005205944          0.026065938         -0.004210633
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.001152457           0.009556988           0.027902683
## Lag 2e+05 -0.027515558          -0.016407706          -0.014807059
## Lag 3e+05 -0.028285236          -0.004044212          -0.014392301
## Lag 4e+05 -0.019349655          -0.001528833          -0.004576124
## Lag 5e+05  0.011268722          -0.007050361          -0.007254703
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000          1.000000000
## Lag 1e+05           -0.00201844         -0.023094334          0.020385408
## Lag 2e+05           -0.01693260         -0.017819638          0.004418618
## Lag 3e+05            0.01486055          0.005929922         -0.010205451
## Lag 4e+05           -0.01138724          0.014734141         -0.002664948
## Lag 5e+05            0.00800532         -0.021725905         -0.012664973
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.002030059          0.022419321          0.008531023
## Lag 2e+05          0.020710362         -0.025762224         -0.028850157
## Lag 3e+05         -0.024766716          0.003318331         -0.023780602
## Lag 4e+05         -0.022539739         -0.016592572          0.006065647
## Lag 5e+05          0.018173312          0.008027753         -0.020830440
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.2922               -0.3009               -1.8090 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -1.1453               -0.5074                0.3174 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.2690               -0.7501                1.3616 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.77013188            0.76348342            0.07045709 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.25209554            0.61188806            0.75092587 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.20443357            0.45318258            0.17333142 
## Joint P-value (lower = worse):  0.5328707 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.6033               -0.4920               -0.7048 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.5829                0.2892               -1.2304 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.7658                0.2324               -0.1326 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.5462842             0.6226907             0.4809219 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.5599855             0.7724257             0.2185452 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.4437810             0.8162342             0.8945129 
## Joint P-value (lower = worse):  0.9199044 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.85935               0.02565              -0.65182 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.15385              -1.41148              -0.13948 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.55235              -0.31387              -1.20225 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.3901500             0.9795355             0.5145173 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.8777245             0.1581028             0.8890712 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.5807070             0.7536166             0.2292666 
## Joint P-value (lower = worse):  0.8138731 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.63820               0.05525               0.14317 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.13206               0.89619               0.19908 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               0.71678               1.29414               0.80979 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.5233416             0.9559410             0.8861596 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.8949358             0.3701497             0.8422030 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.4735115             0.1956158             0.4180612 
## Joint P-value (lower = worse):  0.559907 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.53543              -0.45590              -1.20356 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.01205               0.55669               0.69577 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.49903              -1.01636               1.08642 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.5923512             0.6484638             0.2287589 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.9903885             0.5777396             0.4865742 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.6177612             0.3094575             0.2772949 
## Joint P-value (lower = worse):  0.6567077 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.5923               -1.5416               -0.8391 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -1.5566               -0.8338               -1.4532 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.9545                1.1016               -0.6500 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.1113158             0.1231625             0.4014238 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.1195548             0.4043869             0.1461760 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.3398440             0.2706254             0.5157003 
## Joint P-value (lower = worse):  0.3917235 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.8710                0.1817                0.3839 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                1.0736                1.0140                1.4675 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.3844               -0.5347               -0.1926 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.3837566             0.8558347             0.7010630 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.2830169             0.3105639             0.1422514 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.7006767             0.5928299             0.8472647 
## Joint P-value (lower = worse):  0.476547 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.03613              -0.21532               0.84866 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.34027              -0.54939               0.44299 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               1.32442               0.32812               0.06498 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.9711771             0.8295182             0.3960717 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.7336555             0.5827377             0.6577709 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.1853652             0.7428233             0.9481861 
## Joint P-value (lower = worse):  0.9453042 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 6

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -0.85347 40.043  0.23119        0.22811
## nodefactor.deg.main.1 -1.22360 45.293  0.26150        0.25749
## nodefactor.race..wa.B -0.12907 16.001  0.09238        0.09189
## nodefactor.race..wa.H -0.61343 23.500  0.13568        0.13613
## nodefactor.region.EW  -0.28337 20.058  0.11580        0.11358
## nodefactor.region.OW  -0.50533 38.256  0.22087        0.21823
## nodematch.race..wa.B   0.01832  2.897  0.01672        0.01666
## nodematch.race..wa.H  -0.09230  6.938  0.04006        0.03976
## nodematch.race..wa.O  -0.19218 32.681  0.18869        0.18639
## absdiff.sqrt.age      -0.60442 45.117  0.26048        0.25827
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%      50%    75% 97.5%
## edges                 -79.50 -27.500 -0.50000 26.500 78.50
## nodefactor.deg.main.1 -89.00 -32.000 -1.00000 29.000 89.00
## nodefactor.race..wa.B -31.52 -11.517 -0.51680 10.483 31.48
## nodefactor.race..wa.H -47.34 -16.340 -0.34000 15.660 45.66
## nodefactor.region.EW  -38.48 -13.482 -0.48200 13.518 39.52
## nodefactor.region.OW  -74.59 -26.585 -0.58550 25.415 74.41
## nodematch.race..wa.B   -5.48  -2.480 -0.47985  1.520  6.52
## nodematch.race..wa.H  -13.18  -5.181 -0.18150  4.819 13.82
## nodematch.race..wa.O  -64.08 -22.081 -0.08078 21.919 64.92
## absdiff.sqrt.age      -87.86 -31.258 -1.14773 29.720 88.74
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.75480668
## nodefactor.deg.main.1 0.75480668            1.00000000
## nodefactor.race..wa.B 0.34217222            0.23349580
## nodefactor.race..wa.H 0.46695909            0.40259275
## nodefactor.region.EW  0.42468980            0.33448998
## nodefactor.region.OW  0.67894673            0.51226908
## nodematch.race..wa.B  0.06261356            0.03920251
## nodematch.race..wa.H  0.12390244            0.11529131
## nodematch.race..wa.O  0.78629289            0.57512114
## absdiff.sqrt.age      0.73656977            0.55645693
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.34217222          0.4669590865
## nodefactor.deg.main.1            0.23349580          0.4025927532
## nodefactor.race..wa.B            1.00000000          0.1081000103
## nodefactor.race..wa.H            0.10810001          1.0000000000
## nodefactor.region.EW             0.09725968          0.3013521972
## nodefactor.region.OW             0.21012703          0.2904828655
## nodematch.race..wa.B             0.31320138          0.0000761264
## nodematch.race..wa.H            -0.01485683          0.4976074049
## nodematch.race..wa.O            -0.02361540         -0.0322653364
## absdiff.sqrt.age                 0.25954111          0.3398164295
##                       nodefactor.region.EW nodefactor.region.OW
## edges                           0.42468980           0.67894673
## nodefactor.deg.main.1           0.33448998           0.51226908
## nodefactor.race..wa.B           0.09725968           0.21012703
## nodefactor.race..wa.H           0.30135220           0.29048287
## nodefactor.region.EW            1.00000000           0.12690300
## nodefactor.region.OW            0.12690300           1.00000000
## nodematch.race..wa.B            0.01001047           0.03158038
## nodematch.race..wa.H            0.11680094           0.06469305
## nodematch.race..wa.O            0.29512292           0.55633739
## absdiff.sqrt.age                0.31493189           0.49819399
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                         0.0626135648          0.123902443
## nodefactor.deg.main.1         0.0392025120          0.115291307
## nodefactor.race..wa.B         0.3132013814         -0.014856828
## nodefactor.race..wa.H         0.0000761264          0.497607405
## nodefactor.region.EW          0.0100104720          0.116800945
## nodefactor.region.OW          0.0315803799          0.064693046
## nodematch.race..wa.B          1.0000000000          0.005454956
## nodematch.race..wa.H          0.0054549557          1.000000000
## nodematch.race..wa.O          0.0101588545          0.007022237
## absdiff.sqrt.age              0.0466051182          0.085842705
##                       nodematch.race..wa.O absdiff.sqrt.age
## edges                          0.786292886       0.73656977
## nodefactor.deg.main.1          0.575121137       0.55645693
## nodefactor.race..wa.B         -0.023615400       0.25954111
## nodefactor.race..wa.H         -0.032265336       0.33981643
## nodefactor.region.EW           0.295122921       0.31493189
## nodefactor.region.OW           0.556337391       0.49819399
## nodematch.race..wa.B           0.010158854       0.04660512
## nodematch.race..wa.H           0.007022237       0.08584271
## nodematch.race..wa.O           1.000000000       0.57958994
## absdiff.sqrt.age               0.579589937       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.001106597           0.004582242         -0.0031593475
## Lag 2e+05 -0.020765123          -0.022332441          0.0199925795
## Lag 3e+05  0.020794922           0.020004835         -0.0002757238
## Lag 4e+05 -0.007201211          -0.018799993         -0.0074313649
## Lag 5e+05  0.004522960          -0.043506911          0.0028203454
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.006162301          0.017809876          0.000298485
## Lag 2e+05          -0.011995140         -0.002756002         -0.025201623
## Lag 3e+05          -0.003521245          0.021521112         -0.001060858
## Lag 4e+05           0.004229335         -0.009326315         -0.010209185
## Lag 5e+05          -0.011623757         -0.001300962         -0.005526272
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.002163765          0.006831619        -0.0212108962
## Lag 2e+05         -0.028153463         -0.030185062        -0.0175654164
## Lag 3e+05          0.007521811          0.002784691        -0.0001801002
## Lag 4e+05         -0.004454475         -0.001585238        -0.0108710916
## Lag 5e+05          0.009994769         -0.004331194         0.0110472169
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.001954283
## Lag 2e+05     -0.022332160
## Lag 3e+05      0.005050469
## Lag 4e+05     -0.012282806
## Lag 5e+05      0.013869389
## Chain 2 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05 -0.0157592247          -0.014943574           0.004496101
## Lag 2e+05 -0.0078511055           0.001082632           0.020665876
## Lag 3e+05  0.0034878569           0.003989342          -0.026456928
## Lag 4e+05 -0.0003947242           0.013735763           0.012553970
## Lag 5e+05  0.0046880175          -0.006850831           0.009521430
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.002811276          0.010094332         -0.034619058
## Lag 2e+05          -0.035029463          0.005078504          0.005369494
## Lag 3e+05           0.014030772          0.012939761         -0.046214979
## Lag 4e+05           0.008443736         -0.000629914          0.006578117
## Lag 5e+05          -0.035224011         -0.003356241          0.021876824
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.022449455          0.007968422         -0.002272499
## Lag 2e+05         -0.010427772         -0.021695154         -0.016686202
## Lag 3e+05         -0.020060486          0.009659008         -0.003412187
## Lag 4e+05          0.008044869         -0.011038593         -0.005492871
## Lag 5e+05         -0.009689088          0.005905722          0.010610707
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.002793681
## Lag 2e+05     -0.005485532
## Lag 3e+05      0.006237268
## Lag 4e+05     -0.031217205
## Lag 5e+05     -0.015371519
## Chain 3 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000          1.0000000000            1.00000000
## Lag 1e+05 -0.0261038716         -0.0143784064            0.01647223
## Lag 2e+05  0.0299717772          0.0224651079            0.02910889
## Lag 3e+05  0.0009265022          0.0007632403            0.01663183
## Lag 4e+05  0.0065082343         -0.0113303017           -0.02167512
## Lag 5e+05  0.0225628206          0.0111823803            0.02121583
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.027927516         -0.010515198         -0.026625409
## Lag 2e+05           0.011746960         -0.004136206          0.011301111
## Lag 3e+05           0.002403153          0.006664457         -0.009931964
## Lag 4e+05           0.017458836          0.016436606          0.006236205
## Lag 5e+05          -0.015239173          0.017560236          0.001689468
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000           1.00000000
## Lag 1e+05         -0.033240197         -0.003151408          -0.01888296
## Lag 2e+05         -0.007695941         -0.003126962           0.02079403
## Lag 3e+05         -0.022749205         -0.001460359           0.01243862
## Lag 4e+05          0.012525616          0.013404273           0.02254698
## Lag 5e+05         -0.027669913         -0.004779607           0.02639782
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.030707018
## Lag 2e+05      0.006860491
## Lag 3e+05     -0.005087096
## Lag 4e+05     -0.007608633
## Lag 5e+05      0.013721362
## Chain 4 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.0009702663          0.0211391304          -0.017441653
## Lag 2e+05 -0.0087857946         -0.0006329994          -0.029560734
## Lag 3e+05 -0.0053802787         -0.0075740411           0.023316224
## Lag 4e+05  0.0069513825          0.0020230389          -0.007712845
## Lag 5e+05 -0.0115225249          0.0158596986           0.010603881
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05          -0.008876534           0.02156196          0.003353123
## Lag 2e+05          -0.025098314          -0.01857287         -0.020141823
## Lag 3e+05           0.011013267           0.02863067         -0.006882306
## Lag 4e+05          -0.002887517          -0.03646250         -0.003795336
## Lag 5e+05          -0.024027018           0.02223268         -0.010676339
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05         -0.018087556         -0.036776393         0.0120571166
## Lag 2e+05          0.013791566          0.002308595         0.0050206717
## Lag 3e+05          0.002324272          0.002423513        -0.0067656610
## Lag 4e+05          0.031314132         -0.006402121         0.0006060183
## Lag 5e+05          0.002593423         -0.015622417         0.0017303156
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.021508286
## Lag 2e+05     -0.010984526
## Lag 3e+05      0.005240194
## Lag 4e+05      0.009467763
## Lag 5e+05     -0.008764874
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.010179682          -0.020151141          -0.048314481
## Lag 2e+05  0.023460215           0.009684010          -0.003094904
## Lag 3e+05 -0.035384521          -0.013877904          -0.014216655
## Lag 4e+05 -0.002910053           0.003425068          -0.010953245
## Lag 5e+05 -0.009635453           0.012309316           0.009597170
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05          0.0132127041          0.002654046         -0.015511432
## Lag 2e+05         -0.0057738370          0.003599510          0.008150541
## Lag 3e+05          0.0155891080         -0.030331747         -0.019719850
## Lag 4e+05         -0.0193867571          0.004390452          0.002856458
## Lag 5e+05          0.0005314615         -0.027222517         -0.001882021
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000           1.00000000          1.000000000
## Lag 1e+05          -0.01199087           0.01696358         -0.001644111
## Lag 2e+05          -0.01021342           0.01059156          0.009359877
## Lag 3e+05           0.01416565           0.01430420         -0.006409555
## Lag 4e+05          -0.02330695          -0.02346616         -0.016616755
## Lag 5e+05          -0.01270349           0.00306568         -0.016019942
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.026491880
## Lag 2e+05     -0.002657270
## Lag 3e+05     -0.010595987
## Lag 4e+05     -0.005134211
## Lag 5e+05     -0.006018917
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.002999248          -0.013159909           0.012944411
## Lag 2e+05 -0.002165625          -0.006095738           0.008666305
## Lag 3e+05 -0.016161423           0.004827580           0.009726764
## Lag 4e+05  0.002779210           0.016077519          -0.005547820
## Lag 5e+05 -0.017331309          -0.007370630          -0.015391818
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05           0.008893769          0.009024274         0.0034245407
## Lag 2e+05           0.012506623          0.016839003        -0.0024306402
## Lag 3e+05          -0.009381559          0.003784774        -0.0095820279
## Lag 4e+05          -0.001977041         -0.016428994        -0.0002933984
## Lag 5e+05          -0.008964214         -0.018354700        -0.0035974481
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000           1.00000000
## Lag 1e+05          0.008079179         -0.024600190           0.01502831
## Lag 2e+05         -0.016884256          0.007951379          -0.00541248
## Lag 3e+05          0.015007341         -0.013555652          -0.02967566
## Lag 4e+05          0.019771821          0.020529682          -0.03445465
## Lag 5e+05          0.001967153          0.031308169          -0.01063953
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.006272615
## Lag 2e+05      0.006567439
## Lag 3e+05     -0.025781104
## Lag 4e+05      0.012900642
## Lag 5e+05     -0.013647239
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.017365731          -0.023294594          -0.008089333
## Lag 2e+05 -0.004625232          -0.006158168           0.015073705
## Lag 3e+05 -0.011097677           0.000136385           0.023258328
## Lag 4e+05  0.028041096           0.011212458           0.000191891
## Lag 5e+05 -0.039124006          -0.026723194          -0.018542918
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05          -0.014666548         -0.009883410         0.0040017613
## Lag 2e+05          -0.006878310         -0.005841600        -0.0060386083
## Lag 3e+05          -0.019277717         -0.002313732         0.0003674597
## Lag 4e+05          -0.014201804         -0.016039629        -0.0051094740
## Lag 5e+05          -0.009632885         -0.014515334        -0.0156964469
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.007720369          0.007798922          0.003456800
## Lag 2e+05          0.007868813          0.017402283          0.003281598
## Lag 3e+05          0.015888529         -0.018656641          0.001209753
## Lag 4e+05         -0.012182057         -0.022102180          0.023892960
## Lag 5e+05          0.021287296          0.021764637         -0.018821109
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.001285783
## Lag 2e+05      0.004028142
## Lag 3e+05     -0.004642598
## Lag 4e+05      0.010085015
## Lag 5e+05     -0.025398637
## Chain 8 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.0093937422          -0.011441263         -0.0059252978
## Lag 2e+05 -0.0415312765          -0.032419924          0.0005346151
## Lag 3e+05 -0.0128304975           0.002389485          0.0004486710
## Lag 4e+05 -0.0006671555          -0.027551003         -0.0094440626
## Lag 5e+05  0.0121891473          -0.005770928         -0.0026574826
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.027026064         0.0056360533          0.017495673
## Lag 2e+05          -0.004862280        -0.0195448025         -0.003016671
## Lag 3e+05          -0.024745153         0.0059402277         -0.006669335
## Lag 4e+05          -0.003426496        -0.0163740566         -0.010733134
## Lag 5e+05          -0.006437120        -0.0006692625         -0.003832869
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.009110847          0.002181974        -0.0173067990
## Lag 2e+05          0.022163251          0.021232498        -0.0323793606
## Lag 3e+05         -0.006198292         -0.002375961        -0.0001700499
## Lag 4e+05          0.007422827          0.006826874         0.0199697038
## Lag 5e+05         -0.006100744         -0.006471887         0.0096501335
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.023255432
## Lag 2e+05     -0.035608355
## Lag 3e+05     -0.013568632
## Lag 4e+05      0.002674166
## Lag 5e+05     -0.023235379
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.96657               0.06281               0.56812 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.91022               0.95788               1.33481 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.15383              -0.78921               0.12913 
##      absdiff.sqrt.age 
##               0.33798 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.3337602             0.9499160             0.5699558 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.3627072             0.3381222             0.1819396 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8777418             0.4299916             0.8972527 
##      absdiff.sqrt.age 
##             0.7353790 
## Joint P-value (lower = worse):  0.7830986 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.21127              -0.09902              -0.09480 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.48987              -0.83234              -1.27376 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               1.33319               1.22473               0.11023 
##      absdiff.sqrt.age 
##              -1.41252 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.8326733             0.9211195             0.9244757 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.6242267             0.4052192             0.2027487 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.1824691             0.2206779             0.9122254 
##      absdiff.sqrt.age 
##             0.1577972 
## Joint P-value (lower = worse):  0.1798488 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.45260              -0.01521              -0.49216 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.65535              -0.39828               0.52925 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               0.03038              -0.66350               1.06134 
##      absdiff.sqrt.age 
##               0.27108 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6508381             0.9878640             0.6226046 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.5122443             0.6904204             0.5966318 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.9757601             0.5070129             0.2885352 
##      absdiff.sqrt.age 
##             0.7863277 
## Joint P-value (lower = worse):  0.9910889 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                1.9498                2.2738               -0.6467 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.7675                0.9211                2.0748 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.8804               -0.4424                1.7682 
##      absdiff.sqrt.age 
##                2.0084 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.05120063            0.02298075            0.51781522 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.44277820            0.35702352            0.03800596 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.06004931            0.65820193            0.07702264 
##      absdiff.sqrt.age 
##            0.04460220 
## Joint P-value (lower = worse):  0.1754572 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.1665                0.1385                1.2100 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.8623                0.6305               -0.4384 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                1.6420               -0.2791               -0.6406 
##      absdiff.sqrt.age 
##                0.2308 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.8677495             0.8898609             0.2262711 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.3885421             0.5283662             0.6610985 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.1005803             0.7801660             0.5217870 
##      absdiff.sqrt.age 
##             0.8174422 
## Joint P-value (lower = worse):  0.8234019 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               1.06268               0.39080               0.02638 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -1.21723               0.08918               0.20058 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.03856              -0.72958               2.01093 
##      absdiff.sqrt.age 
##               2.18769 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.28792513            0.69594780            0.97895395 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.22351619            0.92893580            0.84102969 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.96924231            0.46564858            0.04433237 
##      absdiff.sqrt.age 
##            0.02869229 
## Joint P-value (lower = worse):  0.3798001 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.05214               0.66707              -0.76925 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.76263              -0.02265               1.13641 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               1.88462               0.10588               0.87540 
##      absdiff.sqrt.age 
##               0.64644 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.95841885            0.50472982            0.44174315 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.44568204            0.98192901            0.25578323 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##            0.05948127            0.91568148            0.38135872 
##      absdiff.sqrt.age 
##            0.51799648 
## Joint P-value (lower = worse):  0.2800155 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.02933              -0.82762               0.52258 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.49262              -1.30776              -0.43156 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.91192               0.70794              -0.50823 
##      absdiff.sqrt.age 
##              -0.22579 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.9766013             0.4078851             0.6012657 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.6222824             0.1909550             0.6660648 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.3618109             0.4789805             0.6112900 
##      absdiff.sqrt.age 
##             0.8213645 
## Joint P-value (lower = worse):  0.6469447 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 7

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -0.22973 58.328  0.33676        0.35631
## nodefactor.deg.main.1  1.22413 61.089  0.35270        0.36859
## nodefactor.race..wa.B -0.22310 19.622  0.11329        0.11810
## nodefactor.race..wa.H -0.07100 29.713  0.17155        0.18661
## nodefactor.region.EW  -0.21587 24.799  0.14318        0.14870
## nodefactor.region.OW  -1.16113 50.129  0.28942        0.30834
## concurrent             0.14143 52.201  0.30138        0.31728
## nodematch.race..wa.B   0.05588  2.945  0.01700        0.01769
## nodematch.race..wa.H  -0.13033  7.350  0.04243        0.04835
## nodematch.race..wa.O  -0.18755 44.474  0.25677        0.26775
## absdiff.sqrt.age      -0.69388 57.477  0.33184        0.33971
## 
## 2. Quantiles for each variable:
## 
##                          2.5%     25%      50%    75%  97.5%
## edges                 -114.50 -39.500 -0.50000 39.500 113.50
## nodefactor.deg.main.1 -118.02 -40.000  1.00000 43.000 121.00
## nodefactor.race..wa.B  -37.52 -13.517 -0.51680 12.483  38.48
## nodefactor.race..wa.H  -58.34 -20.340 -0.34000 19.660  58.66
## nodefactor.region.EW   -48.48 -17.482 -0.48200 16.518  48.52
## nodefactor.region.OW   -97.59 -35.585 -1.58550 32.415  97.41
## concurrent            -101.00 -35.000  0.00000 35.000 103.00
## nodematch.race..wa.B    -5.48  -2.480 -0.47985  1.520   6.52
## nodematch.race..wa.H   -14.18  -5.181 -0.18150  4.819  14.82
## nodematch.race..wa.O   -87.08 -30.081 -0.08078 29.919  87.92
## absdiff.sqrt.age      -112.09 -39.673 -1.09334 38.218 111.34
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.81648099
## nodefactor.deg.main.1 0.81648099            1.00000000
## nodefactor.race..wa.B 0.40069490            0.29865084
## nodefactor.race..wa.H 0.53958060            0.48444144
## nodefactor.region.EW  0.48690965            0.41004076
## nodefactor.region.OW  0.74926650            0.61203650
## concurrent            0.95213389            0.77489433
## nodematch.race..wa.B  0.07459228            0.04669405
## nodematch.race..wa.H  0.16999105            0.17040702
## nodematch.race..wa.O  0.84241778            0.67524164
## absdiff.sqrt.age      0.84363266            0.69280023
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.40069490            0.53958060
## nodefactor.deg.main.1            0.29865084            0.48444144
## nodefactor.race..wa.B            1.00000000            0.18170016
## nodefactor.race..wa.H            0.18170016            1.00000000
## nodefactor.region.EW             0.16328741            0.36088442
## nodefactor.region.OW             0.27549292            0.38270880
## concurrent                       0.38516732            0.52912571
## nodematch.race..wa.B             0.36249731            0.01471362
## nodematch.race..wa.H             0.02252394            0.56014123
## nodematch.race..wa.O             0.07799820            0.11156935
## absdiff.sqrt.age                 0.34222865            0.45110826
##                       nodefactor.region.EW nodefactor.region.OW concurrent
## edges                           0.48690965            0.7492665  0.9521339
## nodefactor.deg.main.1           0.41004076            0.6120365  0.7748943
## nodefactor.race..wa.B           0.16328741            0.2754929  0.3851673
## nodefactor.race..wa.H           0.36088442            0.3827088  0.5291257
## nodefactor.region.EW            1.00000000            0.2286595  0.4663089
## nodefactor.region.OW            0.22865953            1.0000000  0.7118113
## concurrent                      0.46630893            0.7118113  1.0000000
## nodematch.race..wa.B            0.01253585            0.0454551  0.0738186
## nodematch.race..wa.H            0.13864257            0.1182021  0.1720526
## nodematch.race..wa.O            0.36955361            0.6511509  0.7933305
## absdiff.sqrt.age                0.41026135            0.6298159  0.8019299
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                         0.0745922819          0.169991053
## nodefactor.deg.main.1         0.0466940488          0.170407016
## nodefactor.race..wa.B         0.3624973053          0.022523943
## nodefactor.race..wa.H         0.0147136199          0.560141233
## nodefactor.region.EW          0.0125358518          0.138642565
## nodefactor.region.OW          0.0454550972          0.118202122
## concurrent                    0.0738186008          0.172052639
## nodematch.race..wa.B          1.0000000000          0.008519252
## nodematch.race..wa.H          0.0085192515          1.000000000
## nodematch.race..wa.O          0.0006235499          0.010432993
## absdiff.sqrt.age              0.0660131786          0.140189301
##                       nodematch.race..wa.O absdiff.sqrt.age
## edges                         0.8424177822       0.84363266
## nodefactor.deg.main.1         0.6752416411       0.69280023
## nodefactor.race..wa.B         0.0779981970       0.34222865
## nodefactor.race..wa.H         0.1115693528       0.45110826
## nodefactor.region.EW          0.3695536125       0.41026135
## nodefactor.region.OW          0.6511509003       0.62981593
## concurrent                    0.7933304737       0.80192987
## nodematch.race..wa.B          0.0006235499       0.06601318
## nodematch.race..wa.H          0.0104329931       0.14018930
## nodematch.race..wa.O          1.0000000000       0.71124661
## absdiff.sqrt.age              0.7112466125       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.026594731           0.032844932           0.067000831
## Lag 2e+05 -0.007146452          -0.004570258           0.005985143
## Lag 3e+05 -0.009217947           0.007052100          -0.003528080
## Lag 4e+05  0.012452842           0.001539090           0.012019284
## Lag 5e+05 -0.003453883          -0.002526666           0.011868919
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000         1.0000000000
## Lag 1e+05          0.0513807010          0.022388944         0.0390748728
## Lag 2e+05          0.0181763650          0.015057704        -0.0069757030
## Lag 3e+05         -0.0087564752          0.002449004        -0.0209181698
## Lag 4e+05         -0.0141742713         -0.018485304         0.0006895745
## Lag 5e+05          0.0003097293         -0.009659475        -0.0111137530
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000         1.0000000000          1.000000000
## Lag 1e+05  0.034449741         0.0379530660          0.095050344
## Lag 2e+05 -0.013672465         0.0138515683          0.011267719
## Lag 3e+05 -0.023093278        -0.0001217718         -0.007843089
## Lag 4e+05  0.006647143         0.0343560519          0.018476382
## Lag 5e+05  0.008343528         0.0231546912         -0.001560119
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.020123376      0.009856292
## Lag 2e+05         -0.003684699     -0.000271797
## Lag 3e+05         -0.012797876     -0.009949849
## Lag 4e+05          0.004640038      0.005653602
## Lag 5e+05         -0.005776410     -0.002706751
## Chain 2 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000            1.00000000          1.0000000000
## Lag 1e+05  0.0657338461            0.05896226          0.0105477275
## Lag 2e+05  0.0171972151            0.02660925         -0.0267843687
## Lag 3e+05 -0.0077608049           -0.01439021          0.0152895868
## Lag 4e+05 -0.0241158792           -0.02138720          0.0037343262
## Lag 5e+05  0.0005037434            0.02062716         -0.0001946632
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.080709477           0.04132275          0.042980096
## Lag 2e+05           0.014636621           0.01029496          0.004512051
## Lag 3e+05          -0.008151085          -0.02076592         -0.002276335
## Lag 4e+05          -0.017233894          -0.01551196         -0.015867643
## Lag 5e+05           0.020163499          -0.03161687          0.013709450
##              concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.0000000000          1.000000000          1.000000000
## Lag 1e+05  0.0626179275          0.034816544          0.123535214
## Lag 2e+05  0.0113537309         -0.006808964         -0.007030028
## Lag 3e+05 -0.0017770588         -0.004367898         -0.001170230
## Lag 4e+05 -0.0203639856          0.025515570          0.018507845
## Lag 5e+05  0.0006252052          0.026714747         -0.013437256
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000
## Lag 1e+05           0.07262593      0.016167966
## Lag 2e+05           0.00476256      0.014830420
## Lag 3e+05          -0.02010297     -0.016915644
## Lag 4e+05          -0.01965500     -0.029498841
## Lag 5e+05          -0.02012968     -0.002425949
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.042503906           0.030385894           0.032874779
## Lag 2e+05 -0.008768372           0.009791427           0.002714287
## Lag 3e+05  0.014161524           0.015147140          -0.015318507
## Lag 4e+05 -0.008794261          -0.016325846           0.006624753
## Lag 5e+05 -0.007048150          -0.014257494          -0.012600884
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05           0.055577497          0.008382335         0.0465090591
## Lag 2e+05           0.001772942         -0.011470015        -0.0049638602
## Lag 3e+05           0.005908946         -0.002000963         0.0218300801
## Lag 4e+05          -0.025666849         -0.006282408         0.0004389731
## Lag 5e+05           0.011051621          0.003914083        -0.0060901755
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000           1.00000000
## Lag 1e+05  0.038943233          0.030766773           0.13492348
## Lag 2e+05  0.003488907         -0.011653814           0.04222703
## Lag 3e+05  0.007107461          0.009844802          -0.03135508
## Lag 4e+05 -0.010475917          0.013332071          -0.01403413
## Lag 5e+05  0.008608570          0.001969104          -0.01278452
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.065382705      0.023754470
## Lag 2e+05          0.007962779      0.005361059
## Lag 3e+05          0.006032196     -0.006883518
## Lag 4e+05         -0.004600006     -0.004538942
## Lag 5e+05         -0.009101636      0.014811711
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.069248649           0.050574620          0.0682342611
## Lag 2e+05  0.009167513           0.014296743          0.0102546798
## Lag 3e+05 -0.001734115          -0.013963758         -0.0153888298
## Lag 4e+05 -0.008062514           0.001255802          0.0074614418
## Lag 5e+05  0.006305944           0.010205000         -0.0001554952
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.074093263          0.069292466          0.055938984
## Lag 2e+05           0.020044668          0.012040137          0.001329189
## Lag 3e+05          -0.002089091         -0.007058196          0.002667126
## Lag 4e+05          -0.027695203         -0.000974469         -0.009358984
## Lag 5e+05          -0.012043456          0.005425797          0.001181745
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000         1.0000000000           1.00000000
## Lag 1e+05  0.066686166         0.0405881592           0.15051345
## Lag 2e+05  0.013438472        -0.0049499299           0.02940043
## Lag 3e+05  0.005016311        -0.0054744172          -0.00188604
## Lag 4e+05 -0.004102386        -0.0008073118          -0.03164577
## Lag 5e+05  0.010017967        -0.0151543509           0.00241825
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.038573458      0.044875968
## Lag 2e+05          0.007157131     -0.001626672
## Lag 3e+05         -0.004887081     -0.002532669
## Lag 4e+05         -0.003407678     -0.016066903
## Lag 5e+05          0.013141889      0.010550886
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.044537696           0.027377121          0.0577927496
## Lag 2e+05 -0.004748132          -0.002064222         -0.0150213882
## Lag 3e+05 -0.017072765          -0.010295486         -0.0179429790
## Lag 4e+05  0.001039703          -0.009798092          0.0008094832
## Lag 5e+05  0.012070302           0.010867092          0.0010456336
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.072987293           0.07225280          0.073751171
## Lag 2e+05           0.033230119           0.01450779          0.003343774
## Lag 3e+05           0.006819024           0.02247167         -0.010229074
## Lag 4e+05           0.042658950           0.02682414          0.017181310
## Lag 5e+05           0.021667398           0.01827915          0.024783262
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000          1.000000000
## Lag 1e+05  0.039803709          0.061090857          0.120659473
## Lag 2e+05 -0.002883166         -0.005420350          0.041173475
## Lag 3e+05 -0.006033571         -0.015765729         -0.003438104
## Lag 4e+05 -0.003624401         -0.001083903          0.008387197
## Lag 5e+05  0.006866806         -0.004956215         -0.018368581
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.036118539      0.012277492
## Lag 2e+05         -0.008256993     -0.009132051
## Lag 3e+05         -0.009410662     -0.009906133
## Lag 4e+05         -0.025474315     -0.006566419
## Lag 5e+05         -0.003627699      0.031555326
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.055074457           0.064001059          0.0996163298
## Lag 2e+05  0.008115219           0.019766539          0.0204302553
## Lag 3e+05 -0.000656337          -0.003699488         -0.0465309355
## Lag 4e+05  0.010365652          -0.022629495         -0.0002935412
## Lag 5e+05  0.008118135           0.006553284         -0.0005845760
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.102794767          0.052914096          0.039359432
## Lag 2e+05           0.015796424          0.007348315          0.005213427
## Lag 3e+05          -0.008786293         -0.021360545          0.003840813
## Lag 4e+05          -0.007208975          0.014906126         -0.009529108
## Lag 5e+05           0.004742257          0.009078032          0.012482831
##           concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0     1.00000000           1.00000000          1.000000000
## Lag 1e+05 0.04787745           0.05890178          0.141341827
## Lag 2e+05 0.01514419          -0.01688201          0.026910319
## Lag 3e+05 0.01467193          -0.01096304          0.009324292
## Lag 4e+05 0.02050835          -0.01755032          0.005000421
## Lag 5e+05 0.01132446          -0.01229581          0.017490699
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000      1.000000000
## Lag 1e+05         0.0396995850      0.027081604
## Lag 2e+05        -0.0076330425      0.015768020
## Lag 3e+05         0.0007369334      0.018731212
## Lag 4e+05         0.0217916656     -0.007478782
## Lag 5e+05         0.0046649778     -0.013985648
## Chain 7 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000e+00           1.000000000          1.0000000000
## Lag 1e+05 -8.887286e-05           0.018681766          0.0127677980
## Lag 2e+05  3.485013e-02           0.022656781          0.0010647258
## Lag 3e+05  1.960510e-02           0.016262223          0.0007034834
## Lag 4e+05  2.805256e-02           0.025431251         -0.0039409024
## Lag 5e+05  7.103869e-03           0.002544349         -0.0238735122
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.000000e+00          1.000000000          1.000000000
## Lag 1e+05          6.012580e-02          0.009624640          0.012822093
## Lag 2e+05          1.724395e-02         -0.001331688          0.037379770
## Lag 3e+05          9.396299e-05         -0.012643483          0.041218579
## Lag 4e+05          1.443327e-02          0.025060027         -0.006414426
## Lag 5e+05         -3.063222e-02         -0.032569720          0.034914895
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0     1.000000000          1.000000000          1.000000000
## Lag 1e+05 0.005816846          0.020814726          0.111988588
## Lag 2e+05 0.037633576          0.016749618          0.031720991
## Lag 3e+05 0.010814460         -0.003638374          0.006303549
## Lag 4e+05 0.029558581         -0.013092542         -0.025461024
## Lag 5e+05 0.018595804         -0.022566341         -0.035729556
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05         -0.005028265     -0.016345048
## Lag 2e+05          0.033864507      0.005142851
## Lag 3e+05          0.011090467      0.021271542
## Lag 4e+05          0.011800872      0.016685160
## Lag 5e+05          0.009650436     -0.003433454
## Chain 8 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000          1.0000000000          1.0000000000
## Lag 1e+05  0.06346419          0.0608135640          0.0392303412
## Lag 2e+05  0.01177384         -0.0005993105          0.0104324699
## Lag 3e+05  0.02776525          0.0043970030          0.0008603091
## Lag 4e+05  0.00186726          0.0050071462          0.0186868810
## Lag 5e+05 -0.01197034         -0.0019944427          0.0058200499
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05           0.095898275          0.056620016         0.0564258264
## Lag 2e+05           0.001297143          0.020226772        -0.0101388756
## Lag 3e+05          -0.005374464          0.012895968         0.0245446091
## Lag 4e+05           0.016164676         -0.002138627         0.0146778528
## Lag 5e+05          -0.012980958         -0.003148926         0.0001882081
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000          1.000000000
## Lag 1e+05  0.075081237          0.046215671          0.150032614
## Lag 2e+05  0.017224279         -0.012988403          0.033967440
## Lag 3e+05  0.025420781          0.002284640          0.013536498
## Lag 4e+05  0.004776430         -0.018727145          0.015808716
## Lag 5e+05 -0.006705578          0.006836314          0.001965943
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.049522963      0.026735695
## Lag 2e+05         -0.006286827      0.007610961
## Lag 3e+05          0.023373251      0.051712543
## Lag 4e+05          0.012994976     -0.002956634
## Lag 5e+05         -0.028680070     -0.016793964
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.8570               -1.1736               -3.2826 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.8128               -0.2870               -2.4174 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -1.6804               -2.7784                0.9305 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               -1.9690               -0.8940 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##           0.063316788           0.240541012           0.001028498 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##           0.416328648           0.774077011           0.015631598 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##           0.092870044           0.005462603           0.352114648 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##           0.048958744           0.371327035 
## Joint P-value (lower = worse):  0.01131501 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.02508              -0.63061               1.52053 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.43119              -0.18362              -0.82541 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.61980               2.02574               0.83244 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              -0.58511               0.02940 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.97999255            0.52829399            0.12837858 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.66632707            0.85430789            0.40914040 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.53539127            0.04279178            0.40516031 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.55847541            0.97654323 
## Joint P-value (lower = worse):  0.2948637 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.2973                0.4312                0.9483 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.2471               -1.0634                1.7299 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.1631                1.9274               -0.2507 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##                0.1838                0.6173 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.76626574            0.66635093            0.34299954 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.80486119            0.28760515            0.08364479 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.87044410            0.05392801            0.80206913 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.85414649            0.53700446 
## Joint P-value (lower = worse):  0.2022342 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               1.14313               2.04555               0.74832 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -1.53350               0.37696               1.62992 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               1.43627               0.26614              -0.04452 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               2.01492               1.69873 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.25298530            0.04080063            0.45426857 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.12515297            0.70620525            0.10311755 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.15092689            0.79013170            0.96448984 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.04391312            0.08937016 
## Joint P-value (lower = worse):  0.07807274 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.7419                0.0914                0.8502 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -2.4358               -0.9536               -1.1775 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.3590                0.8377               -1.9707 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               -0.2052                0.6420 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.45814479            0.92717303            0.39522923 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.01485750            0.34029590            0.23899580 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.71961407            0.40217402            0.04875507 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.83742176            0.52086056 
## Joint P-value (lower = worse):  0.03083343 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             -0.869713             -1.652334             -0.625459 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             -0.183427             -0.783863             -0.631163 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             -1.198434             -1.575838             -0.005753 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             -0.949194             -0.906380 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.3844571             0.0984665             0.5316698 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.8544633             0.4331207             0.5279338 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.2307481             0.1150631             0.9954099 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.3425222             0.3647346 
## Joint P-value (lower = worse):  0.6983658 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.08131               0.60608              -0.55426 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               1.21848              -2.37124               0.29835 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.31885              -0.86030               0.28316 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              -0.37512              -0.26605 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.93519649            0.54446140            0.57939887 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.22304080            0.01772852            0.76543721 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.74983727            0.38962186            0.77705638 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.70757319            0.79020021 
## Joint P-value (lower = worse):  0.2535987 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.6526                1.4174               -1.2791 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.8558               -1.1092                0.9931 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.3931               -2.2010               -0.5969 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##                1.6175                0.2823 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.51402403            0.15635796            0.20084651 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.39209280            0.26735687            0.32064190 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.69422969            0.02773637            0.55057427 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.10577329            0.77774150 
## Joint P-value (lower = worse):  0.1463564 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 8

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                  0.53103 58.749  0.33919        0.37806
## nodefactor.deg.main.1  1.35907 61.293  0.35388        0.39316
## nodefactor.race..wa.B -0.24233 19.634  0.11336        0.12626
## nodefactor.race..wa.H -0.02647 29.685  0.17139        0.21330
## nodefactor.region.EW   0.36897 31.080  0.17944        0.26167
## nodefactor.region.OW   0.07723 60.576  0.34974        0.41199
## concurrent             0.43157 52.593  0.30365        0.34138
## nodematch.race..wa.B   0.01072  2.947  0.01702        0.01891
## nodematch.race..wa.H  -0.10673  7.378  0.04260        0.05951
## nodematch.race..wa.O   0.48705 44.642  0.25774        0.28298
## nodematch.region      -0.13870 50.492  0.29151        0.33553
## absdiff.sqrt.age       1.16470 57.758  0.33347        0.34954
## 
## 2. Quantiles for each variable:
## 
##                          2.5%     25%     50%    75%  97.5%
## edges                 -113.50 -39.500  0.5000 40.500 115.50
## nodefactor.deg.main.1 -117.00 -40.000  1.0000 42.000 122.00
## nodefactor.race..wa.B  -38.52 -13.517 -0.5168 12.483  38.48
## nodefactor.race..wa.H  -57.34 -20.340 -0.3400 19.660  58.66
## nodefactor.region.EW   -59.48 -20.482  0.5180 21.518  62.52
## nodefactor.region.OW  -116.59 -41.585  0.4145 40.415 119.41
## concurrent            -101.00 -35.000  0.0000 35.000 104.00
## nodematch.race..wa.B    -5.48  -2.480 -0.4798  1.520   6.52
## nodematch.race..wa.H   -14.18  -5.181 -0.1815  4.819  14.82
## nodematch.race..wa.O   -86.08 -30.081  0.9192 29.919  87.92
## nodematch.region       -98.00 -34.000  0.0000 34.000 100.00
## absdiff.sqrt.age      -111.85 -37.950  1.2638 40.021 114.76
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.deg.main.1
## edges                 1.00000000             0.8191600
## nodefactor.deg.main.1 0.81915999             1.0000000
## nodefactor.race..wa.B 0.41006346             0.3122276
## nodefactor.race..wa.H 0.54023564             0.4803948
## nodefactor.region.EW  0.38492428             0.3289852
## nodefactor.region.OW  0.62787011             0.5196290
## concurrent            0.95408852             0.7792915
## nodematch.race..wa.B  0.07844116             0.0561681
## nodematch.race..wa.H  0.17526505             0.1667161
## nodematch.race..wa.O  0.84654401             0.6819006
## nodematch.region      0.93264345             0.7603297
## absdiff.sqrt.age      0.84514241             0.6911694
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.41006346            0.54023564
## nodefactor.deg.main.1            0.31222763            0.48039476
## nodefactor.race..wa.B            1.00000000            0.18158229
## nodefactor.race..wa.H            0.18158229            1.00000000
## nodefactor.region.EW             0.09196458            0.34182415
## nodefactor.region.OW             0.22450735            0.31467849
## concurrent                       0.39387702            0.52730440
## nodematch.race..wa.B             0.36229037            0.01153152
## nodematch.race..wa.H             0.01112327            0.56567664
## nodematch.race..wa.O             0.09353360            0.11972938
## nodematch.region                 0.39084352            0.48432176
## absdiff.sqrt.age                 0.34818579            0.45815016
##                       nodefactor.region.EW nodefactor.region.OW concurrent
## edges                          0.384924275           0.62787011 0.95408852
## nodefactor.deg.main.1          0.328985216           0.51962904 0.77929154
## nodefactor.race..wa.B          0.091964582           0.22450735 0.39387702
## nodefactor.race..wa.H          0.341824151           0.31467849 0.52730440
## nodefactor.region.EW           1.000000000           0.10214735 0.36981952
## nodefactor.region.OW           0.102147346           1.00000000 0.60085427
## concurrent                     0.369819521           0.60085427 1.00000000
## nodematch.race..wa.B           0.007274091           0.03542512 0.07467856
## nodematch.race..wa.H           0.181277479           0.09344854 0.17332120
## nodematch.race..wa.O           0.281759455           0.55425078 0.80077657
## nodematch.region               0.266151382           0.56609014 0.88956518
## absdiff.sqrt.age               0.324351949           0.53076676 0.80159787
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                         0.0784411592         0.1752650526
## nodefactor.deg.main.1         0.0561680994         0.1667160639
## nodefactor.race..wa.B         0.3622903735         0.0111232669
## nodefactor.race..wa.H         0.0115315171         0.5656766432
## nodefactor.region.EW          0.0072740915         0.1812774787
## nodefactor.region.OW          0.0354251239         0.0934485447
## concurrent                    0.0746785561         0.1733211976
## nodematch.race..wa.B          1.0000000000        -0.0004496876
## nodematch.race..wa.H         -0.0004496876         1.0000000000
## nodematch.race..wa.O          0.0088735798         0.0193204716
## nodematch.region              0.0730543408         0.1517600224
## absdiff.sqrt.age              0.0652970098         0.1544967005
##                       nodematch.race..wa.O nodematch.region
## edges                           0.84654401       0.93264345
## nodefactor.deg.main.1           0.68190058       0.76032972
## nodefactor.race..wa.B           0.09353360       0.39084352
## nodefactor.race..wa.H           0.11972938       0.48432176
## nodefactor.region.EW            0.28175946       0.26615138
## nodefactor.region.OW            0.55425078       0.56609014
## concurrent                      0.80077657       0.88956518
## nodematch.race..wa.B            0.00887358       0.07305434
## nodematch.race..wa.H            0.01932047       0.15176002
## nodematch.race..wa.O            1.00000000       0.79670401
## nodematch.region                0.79670401       1.00000000
## absdiff.sqrt.age                0.71407851       0.78912696
##                       absdiff.sqrt.age
## edges                       0.84514241
## nodefactor.deg.main.1       0.69116944
## nodefactor.race..wa.B       0.34818579
## nodefactor.race..wa.H       0.45815016
## nodefactor.region.EW        0.32435195
## nodefactor.region.OW        0.53076676
## concurrent                  0.80159787
## nodematch.race..wa.B        0.06529701
## nodematch.race..wa.H        0.15449670
## nodematch.race..wa.O        0.71407851
## nodematch.region            0.78912696
## absdiff.sqrt.age            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.107201666          0.0971499174           0.108236388
## Lag 2e+05  0.014100040          0.0128416315           0.008422860
## Lag 3e+05  0.005576469          0.0140954309          -0.009216352
## Lag 4e+05  0.023332174          0.0002030943           0.007243501
## Lag 5e+05 -0.001533415         -0.0118953843           0.008824015
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000         1.0000000000
## Lag 1e+05           0.163778787           0.28669747         0.1476260234
## Lag 2e+05           0.049901266           0.13912365         0.0006437286
## Lag 3e+05           0.010562371           0.07155346        -0.0116874136
## Lag 4e+05           0.010231200           0.05985743         0.0075557524
## Lag 5e+05           0.005987186           0.03706429         0.0286062120
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000           1.00000000
## Lag 1e+05  0.112016609          0.097713811           0.25371630
## Lag 2e+05  0.015151245          0.001842920           0.11858872
## Lag 3e+05 -0.001104045          0.006230627           0.05009370
## Lag 4e+05  0.035500574          0.012103183           0.01966305
## Lag 5e+05 -0.001028632         -0.021439497           0.01219816
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000     1.0000000000
## Lag 1e+05          0.092803380      0.122187889     0.0427827240
## Lag 2e+05          0.016195844      0.026567102    -0.0009037168
## Lag 3e+05         -0.007242533      0.008253839     0.0143826664
## Lag 4e+05          0.003622590      0.027013528     0.0181091746
## Lag 5e+05         -0.021767146     -0.003835779    -0.0087396447
## Chain 2 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0     1.000000000           1.000000000            1.00000000
## Lag 1e+05 0.109483797           0.101251525            0.09625910
## Lag 2e+05 0.011128611           0.024476823            0.01298851
## Lag 3e+05 0.002665121           0.011285788            0.02652923
## Lag 4e+05 0.017711526           0.018968486            0.04145514
## Lag 5e+05 0.026776438           0.002272684           -0.01001557
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000          1.000000000
## Lag 1e+05            0.16601285          0.302706621          0.127943744
## Lag 2e+05            0.03478950          0.144302685          0.021396734
## Lag 3e+05            0.01793421          0.050748301          0.001967084
## Lag 4e+05            0.02337138          0.034551100          0.008524592
## Lag 5e+05            0.02552735          0.006667197          0.023244744
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000           1.00000000
## Lag 1e+05  0.119560309          0.089281817           0.27440042
## Lag 2e+05  0.020815618          0.051195986           0.09348248
## Lag 3e+05 -0.009261797         -0.003573604           0.06827406
## Lag 4e+05  0.028219351         -0.008546737           0.04957012
## Lag 5e+05  0.029963272          0.006160104           0.01711085
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0             1.0000000000       1.00000000       1.00000000
## Lag 1e+05         0.1016900104       0.13409035       0.03802399
## Lag 2e+05         0.0356024484       0.02371235      -0.00821082
## Lag 3e+05         0.0129394108       0.01317740      -0.01378998
## Lag 4e+05         0.0001716103       0.01926763       0.02880678
## Lag 5e+05         0.0217029868       0.03478560       0.01483852
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.126336562           0.101498897           0.125251897
## Lag 2e+05  0.041907203           0.030382937           0.031745199
## Lag 3e+05  0.009712666           0.012759350           0.007379344
## Lag 4e+05 -0.022044748          -0.008285868           0.013565618
## Lag 5e+05  0.022528713           0.032942518           0.015810078
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000          1.000000000
## Lag 1e+05            0.16232631          0.309592242          0.161755916
## Lag 2e+05            0.07692094          0.134562530          0.045473114
## Lag 3e+05            0.06931911          0.071013693          0.033528231
## Lag 4e+05            0.04593953          0.060045219          0.006267326
## Lag 5e+05            0.02426495         -0.005653156          0.031912670
##              concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000e+00           1.00000000           1.00000000
## Lag 1e+05  1.312637e-01           0.10203496           0.28846946
## Lag 2e+05  4.626428e-02           0.03124067           0.15734713
## Lag 3e+05 -1.666307e-05           0.01904118           0.08224872
## Lag 4e+05 -3.014642e-02          -0.02913287           0.05159424
## Lag 5e+05  1.231588e-02          -0.02779936           0.04250978
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000     1.0000000000
## Lag 1e+05          0.114618899      0.153102400     0.0694198231
## Lag 2e+05          0.026375828      0.054678148     0.0186770798
## Lag 3e+05         -0.003540117      0.012205929     0.0041772899
## Lag 4e+05         -0.009114667     -0.008560262     0.0003404308
## Lag 5e+05          0.005599510      0.026265049     0.0121723950
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.120201896           0.097886242           0.106091243
## Lag 2e+05  0.014825848           0.003454529           0.024613983
## Lag 3e+05 -0.017122334          -0.042318881           0.003035207
## Lag 4e+05 -0.001079834          -0.010463660          -0.010184264
## Lag 5e+05 -0.013157262          -0.004095242          -0.004323221
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.199241992           0.27098927          0.185106145
## Lag 2e+05           0.094638140           0.13304331          0.050066961
## Lag 3e+05           0.008045886           0.06816545          0.028596250
## Lag 4e+05           0.033101716           0.02291191          0.023474329
## Lag 5e+05           0.007509799           0.01710397         -0.007892857
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000           1.00000000          1.000000000
## Lag 1e+05  0.133336534           0.10022500          0.277499709
## Lag 2e+05  0.020751348           0.02279752          0.104614556
## Lag 3e+05 -0.014347763           0.01046665          0.046383356
## Lag 4e+05 -0.008386051          -0.00422081          0.028871447
## Lag 5e+05 -0.021925149          -0.01226926          0.002295727
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.074173395      0.148285040      0.070126425
## Lag 2e+05          0.016795840      0.020865496      0.007446260
## Lag 3e+05         -0.021248376     -0.001025551     -0.027151866
## Lag 4e+05          0.021954321      0.006396210     -0.005411577
## Lag 5e+05         -0.006064815     -0.001605445     -0.001306567
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.081029587           0.082190838           0.131134103
## Lag 2e+05  0.040711879           0.041507052           0.021828635
## Lag 3e+05  0.001614596           0.013151039          -0.003811463
## Lag 4e+05  0.005749879           0.024894959          -0.009028895
## Lag 5e+05 -0.006182521          -0.008042022          -0.007635748
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.142935789           0.26123147          0.150433191
## Lag 2e+05           0.040383638           0.13005822          0.068204881
## Lag 3e+05           0.018518151           0.08126260          0.022882479
## Lag 4e+05           0.002141269           0.04263484         -0.008660360
## Lag 5e+05           0.019036370           0.01858960         -0.002259316
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000          1.000000000
## Lag 1e+05  0.097263039          0.105377061          0.231216144
## Lag 2e+05  0.027271828          0.030094375          0.097421184
## Lag 3e+05  0.006747019          0.020191085          0.016413950
## Lag 4e+05  0.012507585          0.005931328          0.006130820
## Lag 5e+05 -0.005438063          0.021249095          0.002633182
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.079897323      0.112369095      0.048490587
## Lag 2e+05          0.052450702      0.037529195      0.018485322
## Lag 3e+05          0.011074115     -0.002806102      0.003823390
## Lag 4e+05          0.016225260     -0.006719477      0.002929638
## Lag 5e+05          0.003504017     -0.011123304     -0.021218138
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.086454475           0.082161611           0.101181456
## Lag 2e+05  0.008039614           0.021383130           0.016329711
## Lag 3e+05 -0.026259255          -0.003044588          -0.002778010
## Lag 4e+05  0.020614632           0.017551636           0.008748552
## Lag 5e+05 -0.032935917          -0.006550458           0.008419701
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000           1.00000000           1.00000000
## Lag 1e+05            0.15546598           0.24566135           0.13200727
## Lag 2e+05            0.04360704           0.11812466           0.01337538
## Lag 3e+05            0.03018067           0.06519326          -0.02959334
## Lag 4e+05            0.04449853           0.04170263           0.03152186
## Lag 5e+05           -0.02088377           0.01364193          -0.02629878
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.00000000          1.000000000           1.00000000
## Lag 1e+05  0.09475195          0.080448841           0.26200450
## Lag 2e+05  0.02045754          0.013264774           0.12525730
## Lag 3e+05 -0.01013718          0.007592905           0.06476571
## Lag 4e+05  0.01125491         -0.008119413           0.03404894
## Lag 5e+05 -0.02468385         -0.028725901           0.02232980
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000      1.000000000
## Lag 1e+05           0.08053239      0.126961167      0.040711812
## Lag 2e+05           0.00345948      0.023130790     -0.001576178
## Lag 3e+05          -0.02683426     -0.014206564     -0.012923134
## Lag 4e+05           0.01149774      0.006422939      0.026248282
## Lag 5e+05          -0.03189219     -0.026683741     -0.037923408
## Chain 7 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0     1.000000000            1.00000000           1.000000000
## Lag 1e+05 0.096792969            0.11113128           0.083560925
## Lag 2e+05 0.017842233            0.04733173           0.014930625
## Lag 3e+05 0.009574316            0.01501156           0.005653635
## Lag 4e+05 0.026775832            0.03083417          -0.003235266
## Lag 5e+05 0.007626324            0.02577537           0.029339061
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000           1.00000000
## Lag 1e+05           0.181114497          0.282609336           0.14955591
## Lag 2e+05           0.044192622          0.114369516           0.03029538
## Lag 3e+05           0.016826271          0.084351198           0.03595789
## Lag 4e+05           0.007323654          0.047588068           0.05080531
## Lag 5e+05          -0.001127711         -0.007977717          -0.02016079
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0     1.000000000           1.00000000           1.00000000
## Lag 1e+05 0.111596994           0.08726044           0.25894485
## Lag 2e+05 0.031272312          -0.01442282           0.11047496
## Lag 3e+05 0.005521659           0.01577971           0.07454468
## Lag 4e+05 0.009281784           0.01624964           0.03173569
## Lag 5e+05 0.002316377          -0.00348819          -0.00546960
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.080784324      0.112488932      0.054827097
## Lag 2e+05          0.010630917      0.018465427      0.008611661
## Lag 3e+05          0.006789876      0.007122142     -0.001841955
## Lag 4e+05          0.022172887      0.033279227      0.017149874
## Lag 5e+05          0.001537216      0.002643699      0.018909565
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.077946046           0.088379488           0.122723768
## Lag 2e+05 -0.002838999           0.023338619           0.024761710
## Lag 3e+05  0.034946154           0.022071148           0.003625276
## Lag 4e+05 -0.003233809          -0.001029780           0.028942622
## Lag 5e+05  0.021136814           0.008303228          -0.015399237
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.172593860           0.27485659          0.136182880
## Lag 2e+05           0.051720138           0.14337077          0.020233661
## Lag 3e+05          -0.009865532           0.08898305          0.037647365
## Lag 4e+05          -0.026318233           0.03037949          0.009331581
## Lag 5e+05           0.015779880           0.01801809         -0.003857793
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000          1.000000000           1.00000000
## Lag 1e+05  0.093028621          0.100040143           0.29085724
## Lag 2e+05 -0.002795369          0.030215289           0.13519533
## Lag 3e+05  0.039712943          0.001731396           0.07155334
## Lag 4e+05  0.001773425          0.030241291           0.04791766
## Lag 5e+05  0.025153124         -0.021444244           0.02397066
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.059384559      0.112640183      0.024909529
## Lag 2e+05         -0.002925037      0.003830332     -0.017990270
## Lag 3e+05          0.025524850      0.041435418      0.028425693
## Lag 4e+05          0.007552126      0.007105551      0.013858127
## Lag 5e+05          0.036137375      0.023169485      0.008951136
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.27203               0.43059              -0.01140 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               1.25382               1.08425              -1.12831 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.02260              -0.11004               0.48603 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##              -0.38023               0.38935              -0.06455 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7855995             0.6667647             0.9909007 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.2099061             0.2782524             0.2591904 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9819700             0.9123816             0.6269460 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.7037784             0.6970192             0.9485360 
## Joint P-value (lower = worse):  0.9514591 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.43206               0.30302               0.90190 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.74888               0.07457               2.02414 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.14296               2.20000              -0.25170 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               0.86021               0.31337               0.29216 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.66570064            0.76187488            0.36711069 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.45392924            0.94055668            0.04295529 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.88631791            0.02780711            0.80127021 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.38967231            0.75400051            0.77016343 
## Joint P-value (lower = worse):  0.2285956 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.3895               -0.1495               -0.3312 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.2346               -0.6836               -0.1458 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.3144                0.5468                0.4957 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               -0.2068               -0.1197                0.4561 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6969024             0.8811465             0.7404566 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.8145370             0.4942003             0.8840979 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.7532402             0.5845497             0.6201312 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.8361410             0.9047596             0.6483450 
## Joint P-value (lower = worse):  0.9735093 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.2518               -0.6784               -0.5240 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.6681               -1.1866               -1.1048 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -1.4056                1.2337                0.6297 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               -1.1925               -1.4618               -1.2269 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.2106279             0.4975343             0.6003116 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.5040847             0.2354013             0.2692553 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.1598567             0.2173122             0.5288927 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.2330711             0.1437902             0.2198691 
## Joint P-value (lower = worse):  0.4545643 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.4483                0.6562               -0.8178 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -1.3581               -0.5710               -1.3790 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.4832                0.6111               -0.3838 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##                0.2026               -0.3605                0.6986 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6539200             0.5116727             0.4134578 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.1744462             0.5680209             0.1679053 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.6289549             0.5411305             0.7011286 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.8394658             0.7184949             0.4847829 
## Joint P-value (lower = worse):  0.1856638 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.19158               0.12359              -1.27909 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.44871              -0.47010              -0.03950 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.08826              -1.53536              -0.30877 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               0.32399               0.16393               0.43820 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.8480703             0.9016364             0.2008661 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.6536384             0.6382869             0.9684938 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9296716             0.1246945             0.7574953 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.7459437             0.8697835             0.6612424 
## Joint P-value (lower = worse):  0.9257134 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -1.10830              -1.04594              -0.73286 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.67201              -0.02663               0.79794 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.71822              -0.98229               0.54798 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##              -1.81875              -0.99279              -1.07573 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.26773156            0.29558908            0.46364558 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.50157519            0.97875414            0.42490609 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.47262261            0.32595633            0.58370393 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.06894968            0.32081087            0.28204726 
## Joint P-value (lower = worse):  0.2512177 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.13507              -0.33782              -1.69603 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -1.72321              -0.54795              -0.01553 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.14909              -1.64587              -2.24098 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               0.72660               0.26310               0.17459 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.89255988            0.73549994            0.08988060 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.08485034            0.58372629            0.98760884 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.88148542            0.09979034            0.02502725 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.46747011            0.79247398            0.86140188 
## Joint P-value (lower = worse):  0.3494532 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Summary of model fit

Model 1

summary(est.p.buildup.bal[[1]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55fde6a2dd28>
## 
## Iterations:  94 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -9.92021    0.02472      0  <1e-04 ***
## deg3+                      -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.I     -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R     -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 2

summary(est.p.buildup.bal[[2]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55fe0a9ecac0>
## 
## Iterations:  81 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC %  p-value    
## edges                  -10.06398    0.02999      0  < 1e-04 ***
## nodefactor.race..wa.B    0.24787    0.06619      0 0.000181 ***
## nodefactor.race..wa.H    0.45242    0.04836      0  < 1e-04 ***
## deg3+                       -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 3

summary(est.p.buildup.bal[[3]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55fe28b03d98>
## 
## Iterations:  95 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -10.5478     0.1540      0 < 1e-04 ***
## nodefactor.race..wa.B    0.6644     0.1385      0 < 1e-04 ***
## nodefactor.race..wa.H    0.8716     0.1472      0 < 1e-04 ***
## nodematch.race..wa.B    -0.5199     0.3776      0 0.16852    
## nodematch.race..wa.H    -0.2345     0.2072      0 0.25788    
## nodematch.race..wa.O     0.5017     0.1558      0 0.00128 ** 
## deg3+                      -Inf     0.0000      0 < 1e-04 ***
## nodematch.role.class.I     -Inf     0.0000      0 < 1e-04 ***
## nodematch.role.class.R     -Inf     0.0000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 4

summary(est.p.buildup.bal[[4]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55fe46dd2c40>
## 
## Iterations:  90 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                  -10.42812    0.15677      0 < 1e-04 ***
## nodefactor.deg.main.1   -0.13633    0.03373      0 < 1e-04 ***
## nodefactor.race..wa.B    0.64962    0.13916      0 < 1e-04 ***
## nodefactor.race..wa.H    0.88359    0.14764      0 < 1e-04 ***
## nodematch.race..wa.B    -0.51254    0.37728      0 0.17430    
## nodematch.race..wa.H    -0.23125    0.20676      0 0.26338    
## nodematch.race..wa.O     0.49945    0.15667      0 0.00143 ** 
## deg3+                       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 5

summary(est.p.buildup.bal[[5]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55fe651e7160>
## 
## Iterations:  110 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                  -1.043e+01  1.581e-01      0 < 1e-04 ***
## nodefactor.deg.main.1  -1.365e-01  3.396e-02      0 < 1e-04 ***
## nodefactor.race..wa.B   6.511e-01  1.380e-01      0 < 1e-04 ***
## nodefactor.race..wa.H   8.855e-01  1.470e-01      0 < 1e-04 ***
## nodefactor.region.EW   -2.614e-03  5.676e-02      0 0.96327    
## nodefactor.region.OW    5.361e-04  3.685e-02      0 0.98839    
## nodematch.race..wa.B   -5.175e-01  3.807e-01      0 0.17396    
## nodematch.race..wa.H   -2.343e-01  2.070e-01      0 0.25770    
## nodematch.race..wa.O    5.014e-01  1.553e-01      0 0.00124 ** 
## deg3+                        -Inf  0.000e+00      0 < 1e-04 ***
## nodematch.role.class.I       -Inf  0.000e+00      0 < 1e-04 ***
## nodematch.role.class.R       -Inf  0.000e+00      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 6

summary(est.p.buildup.bal[[6]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + absdiff("sqrt.age") + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55fe8371dfc0>
## 
## Iterations:  104 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                  -9.8746130  0.1605361      0 < 1e-04 ***
## nodefactor.deg.main.1  -0.1333392  0.0338123      0 < 1e-04 ***
## nodefactor.race..wa.B   0.6648410  0.1384549      0 < 1e-04 ***
## nodefactor.race..wa.H   0.8824286  0.1470090      0 < 1e-04 ***
## nodefactor.region.EW    0.0008487  0.0573078      0 0.98818    
## nodefactor.region.OW   -0.0007959  0.0367502      0 0.98272    
## nodematch.race..wa.B   -0.5239213  0.3757057      0 0.16317    
## nodematch.race..wa.H   -0.2337111  0.2078797      0 0.26090    
## nodematch.race..wa.O    0.5016253  0.1557783      0 0.00128 ** 
## absdiff.sqrt.age       -0.5707063  0.0327775      0 < 1e-04 ***
## deg3+                        -Inf  0.0000000      0 < 1e-04 ***
## nodematch.role.class.I       -Inf  0.0000000      0 < 1e-04 ***
## nodematch.role.class.R       -Inf  0.0000000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 7

summary(est.p.buildup.bal[[7]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + concurrent + 
##     nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") + 
##     degrange(from = 3) + offset(nodematch("role.class", diff = TRUE, 
##     keep = 1:2))
## <environment: 0x55fea1cd7b10>
## 
## Iterations:  63 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                  -1.169e+01  1.672e-01      0 < 1e-04 ***
## nodefactor.deg.main.1  -9.259e-02  2.851e-02      0 0.00117 ** 
## nodefactor.race..wa.B   5.958e-01  1.345e-01      0 < 1e-04 ***
## nodefactor.race..wa.H   7.436e-01  1.449e-01      0 < 1e-04 ***
## nodefactor.region.EW    5.817e-04  4.796e-02      0 0.99032    
## nodefactor.region.OW   -8.268e-04  3.104e-02      0 0.97875    
## concurrent              2.503e+00  6.280e-02      0 < 1e-04 ***
## nodematch.race..wa.B   -5.234e-01  3.760e-01      0 0.16397    
## nodematch.race..wa.H   -2.307e-01  2.072e-01      0 0.26557    
## nodematch.race..wa.O    5.011e-01  1.558e-01      0 0.00130 ** 
## absdiff.sqrt.age       -5.455e-01  3.241e-02      0 < 1e-04 ***
## deg3+                        -Inf  0.000e+00      0 < 1e-04 ***
## nodematch.role.class.I       -Inf  0.000e+00      0 < 1e-04 ***
## nodematch.role.class.R       -Inf  0.000e+00      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 8

summary(est.p.buildup.bal[[8]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + concurrent + 
##     nodematch("race..wa", diff = TRUE) + nodematch("region", 
##     diff = FALSE) + absdiff("sqrt.age") + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55feba616980>
## 
## Iterations:  67 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC %  p-value    
## edges                  -13.18237    0.17453      0  < 1e-04 ***
## nodefactor.deg.main.1   -0.09252    0.02857      0 0.001201 ** 
## nodefactor.race..wa.B    0.62960    0.13436      0  < 1e-04 ***
## nodefactor.race..wa.H    0.78694    0.14419      0  < 1e-04 ***
## nodefactor.region.EW     0.64069    0.03785      0  < 1e-04 ***
## nodefactor.region.OW     0.22781    0.02186      0  < 1e-04 ***
## concurrent               2.50387    0.06360      0  < 1e-04 ***
## nodematch.race..wa.B    -0.59941    0.37661      0 0.111474    
## nodematch.race..wa.H    -0.33275    0.20642      0 0.106962    
## nodematch.race..wa.O     0.53795    0.15579      0 0.000554 ***
## nodematch.region         1.86437    0.05768      0  < 1e-04 ***
## absdiff.sqrt.age        -0.54591    0.03241      0  < 1e-04 ***
## deg3+                       -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Network diagnostics

Model 1

(dx_pers1 <- netdx(est.p.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                        Target Sim Mean Pct Diff Sim SD
## edges                  2017.5 2062.385    0.022 40.346
## nodefactor.deg.main.1      NA 1845.361       NA 48.510
## nodefactor.race..wa.B      NA  248.598       NA 14.635
## nodefactor.race..wa.H      NA  448.375       NA 20.592
## nodefactor.region.EW       NA  416.428       NA 20.456
## nodefactor.region.OW       NA 1353.488       NA 37.456
## concurrent                 NA  629.675       NA 28.457
## nodematch.race..wa.B       NA    7.228       NA  2.763
## nodematch.race..wa.H       NA   23.651       NA  4.575
## nodematch.race..wa.O       NA 1424.092       NA 35.638
## nodematch.region           NA  916.896       NA 30.829
## absdiff.sqrt.age           NA 2352.860       NA 58.252
## deg3+                      NA    0.000       NA  0.000
## nodematch.role.class.I     NA    0.000       NA  0.000
## nodematch.role.class.R     NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.619   -0.030 30.094
## Pct Edges Diss  0.032    0.032   -0.002  0.004
plot(dx_pers1, type="formation")

plot(dx_pers1, type="duration")

plot(dx_pers1, type="dissolution")

Model 2

(dx_pers2 <- netdx(est.p.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2056.056    0.019 39.921
## nodefactor.deg.main.1        NA 1854.252       NA 47.558
## nodefactor.race..wa.B   285.517  291.098    0.020 17.202
## nodefactor.race..wa.H   605.340  614.269    0.015 23.197
## nodefactor.region.EW         NA  434.546       NA 21.218
## nodefactor.region.OW         NA 1335.572       NA 39.994
## concurrent                   NA  633.857       NA 28.000
## nodematch.race..wa.B         NA   10.109       NA  3.314
## nodematch.race..wa.H         NA   45.909       NA  7.242
## nodematch.race..wa.O         NA 1250.107       NA 31.136
## nodematch.region             NA  905.326       NA 28.476
## absdiff.sqrt.age             NA 2351.321       NA 58.872
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.595   -0.031 30.082
## Pct Edges Diss  0.032    0.032   -0.001  0.004
plot(dx_pers2, type="formation")

plot(dx_pers2, type="duration")

plot(dx_pers2, type="dissolution")

Model 3

(dx_pers3 <- netdx(est.p.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2056.244    0.019 42.971
## nodefactor.deg.main.1        NA 1852.613       NA 48.895
## nodefactor.race..wa.B   285.517  288.433    0.010 17.041
## nodefactor.race..wa.H   605.340  615.979    0.018 23.869
## nodefactor.region.EW         NA  436.181       NA 20.177
## nodefactor.region.OW         NA 1335.686       NA 38.562
## concurrent                   NA  633.555       NA 28.758
## nodematch.race..wa.B      8.480    8.891    0.048  3.014
## nodematch.race..wa.H     51.181   51.955    0.015  6.664
## nodematch.race..wa.O   1247.081 1274.925    0.022 34.913
## nodematch.region             NA  904.744       NA 29.157
## absdiff.sqrt.age             NA 2352.026       NA 63.986
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.578   -0.031 30.065
## Pct Edges Diss  0.032    0.032    0.000  0.004
plot(dx_pers3, type="formation")

plot(dx_pers3, type="duration")

plot(dx_pers3, type="dissolution")

Model 4

(dx_pers4 <- netdx(est.p.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2053.698    0.018 37.942
## nodefactor.deg.main.1  1699.000 1730.191    0.018 46.818
## nodefactor.race..wa.B   285.517  289.530    0.014 15.258
## nodefactor.race..wa.H   605.340  616.321    0.018 23.595
## nodefactor.region.EW         NA  433.458       NA 20.344
## nodefactor.region.OW         NA 1331.695       NA 36.791
## concurrent                   NA  633.139       NA 28.303
## nodematch.race..wa.B      8.480    8.383   -0.011  2.809
## nodematch.race..wa.H     51.181   52.227    0.020  7.220
## nodematch.race..wa.O   1247.081 1269.715    0.018 31.072
## nodematch.region             NA  905.859       NA 28.228
## absdiff.sqrt.age             NA 2350.070       NA 57.350
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.557   -0.032 29.952
## Pct Edges Diss  0.032    0.032    0.001  0.004
plot(dx_pers4, type="formation")

plot(dx_pers4, type="duration")

plot(dx_pers4, type="dissolution")

Model 5

(dx_pers5 <- netdx(est.p.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2055.139    0.019 41.661
## nodefactor.deg.main.1  1699.000 1734.556    0.021 45.546
## nodefactor.race..wa.B   285.517  290.455    0.017 15.865
## nodefactor.race..wa.H   605.340  614.726    0.016 23.737
## nodefactor.region.EW    424.482  431.884    0.017 19.816
## nodefactor.region.OW   1312.585 1337.385    0.019 37.674
## concurrent                   NA  636.386       NA 28.390
## nodematch.race..wa.B      8.480    8.236   -0.029  2.493
## nodematch.race..wa.H     51.181   51.513    0.006  6.940
## nodematch.race..wa.O   1247.081 1271.105    0.019 32.711
## nodematch.region             NA  907.201       NA 29.550
## absdiff.sqrt.age             NA 2346.399       NA 59.986
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.597   -0.031 30.031
## Pct Edges Diss  0.032    0.032   -0.001  0.004
plot(dx_pers5, type="formation")

plot(dx_pers5, type="duration")

plot(dx_pers5, type="dissolution")

Model 6

(dx_pers6 <- netdx(est.p.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2057.085    0.020 39.382
## nodefactor.deg.main.1  1699.000 1736.693    0.022 43.621
## nodefactor.race..wa.B   285.517  291.456    0.021 17.076
## nodefactor.race..wa.H   605.340  618.041    0.021 23.043
## nodefactor.region.EW    424.482  430.752    0.015 19.374
## nodefactor.region.OW   1312.585 1338.213    0.020 37.984
## concurrent                   NA  640.503       NA 28.348
## nodematch.race..wa.B      8.480    8.686    0.024  2.921
## nodematch.race..wa.H     51.181   52.035    0.017  7.142
## nodematch.race..wa.O   1247.081 1270.407    0.019 31.552
## nodematch.region             NA  910.154       NA 28.594
## absdiff.sqrt.age       1664.841 1694.914    0.018 46.359
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.625   -0.030 30.066
## Pct Edges Diss  0.032    0.032   -0.002  0.004
plot(dx_pers6, type="formation")

plot(dx_pers6, type="duration")

plot(dx_pers6, type="dissolution")

Model 7

(dx_pers7 <- netdx(est.p.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2152.499    0.067 68.509
## nodefactor.deg.main.1  1699.000 1816.857    0.069 62.499
## nodefactor.race..wa.B   285.517  302.048    0.058 20.343
## nodefactor.race..wa.H   605.340  640.471    0.058 31.457
## nodefactor.region.EW    424.482  454.200    0.070 25.429
## nodefactor.region.OW   1312.585 1397.217    0.064 58.838
## concurrent             1384.000 1465.939    0.059 59.773
## nodematch.race..wa.B      8.480    8.940    0.054  2.873
## nodematch.race..wa.H     51.181   53.670    0.049  7.709
## nodematch.race..wa.O   1247.081 1336.646    0.072 51.447
## nodematch.region             NA  950.254       NA 35.454
## absdiff.sqrt.age       1664.841 1777.393    0.068 66.065
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.543   -0.033 29.931
## Pct Edges Diss  0.032    0.032    0.000  0.004
plot(dx_pers7, type="formation")

plot(dx_pers7, type="duration")

plot(dx_pers7, type="dissolution")

Model 8

(dx_pers8 <- netdx(est.p.buildup.bal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2017.500 2149.819    0.066 61.549
## nodefactor.deg.main.1  1699.000 1816.503    0.069 66.089
## nodefactor.race..wa.B   285.517  301.143    0.055 20.169
## nodefactor.race..wa.H   605.340  641.056    0.059 29.771
## nodefactor.region.EW    424.482  448.157    0.056 32.088
## nodefactor.region.OW   1312.585 1401.669    0.068 69.290
## concurrent             1384.000 1463.627    0.058 54.676
## nodematch.race..wa.B      8.480    8.457   -0.003  2.744
## nodematch.race..wa.H     51.181   53.844    0.052  7.910
## nodematch.race..wa.O   1247.081 1333.778    0.070 47.333
## nodematch.region       1614.000 1722.092    0.067 51.457
## absdiff.sqrt.age       1664.841 1774.170    0.066 58.189
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.552   -0.032 30.002
## Pct Edges Diss  0.032    0.032    0.000  0.004
plot(dx_pers8, type="formation")

plot(dx_pers8, type="duration")

plot(dx_pers8, type="dissolution")